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François Boulogne 2025-05-27 16:21:16 +02:00
parent d321fce459
commit d5fae56eb4
1 changed files with 60 additions and 69 deletions
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129
optifik/scheludko.py
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@ -63,24 +63,9 @@ def thickness_scheludko_at_order(wavelengths,
return prefactor * (term1 + term2) return prefactor * (term1 + term2)
"""
Calculates the Delta value for arrays of wavelengths, thicknesses h and r_indexs n.
Parameters:
- wavelengths: array_like (or float), wavelengths λ
- thickness : array_like (or float), thicknesses h
- interference_order : int, interference order
- refractive_index : array_like (or float), refractive r_indexs n
Returns:
- delta: ndarray of corresponding Δ values
"""
def Delta(wavelengths, thickness, interference_order, refractive_index): def Delta(wavelengths, thickness, interference_order, refractive_index):
""" """
Compute the Delta values.
Parameters Parameters
---------- ----------
@ -95,8 +80,8 @@ def Delta(wavelengths, thickness, interference_order, refractive_index):
Returns Returns
------- -------
TYPE ndarray
DESCRIPTION. Delta values.
""" """
@ -125,7 +110,6 @@ def Delta(wavelengths, thickness, interference_order, refractive_index):
return (A * (1 + alpha)) / (1 + A * alpha) return (A * (1 + alpha)) / (1 + A * alpha)
def Delta_fit(xdata, thickness, interference_order): def Delta_fit(xdata, thickness, interference_order):
""" """
Wrapper on Delta() for curve_fit. Wrapper on Delta() for curve_fit.
@ -133,7 +117,7 @@ def Delta_fit(xdata, thickness, interference_order):
Parameters Parameters
---------- ----------
xdata : tuple xdata : tuple
(wavelengths, n) (wavelengths, refractive_index)
thickness : array_like (or float) thickness : array_like (or float)
Film thickness. Film thickness.
interference_order : int interference_order : int
@ -145,8 +129,8 @@ def Delta_fit(xdata, thickness, interference_order):
Delta values. Delta values.
""" """
lambdas, n = xdata lambdas, r_index = xdata
return Delta(lambdas, thickness, interference_order, n) return Delta(lambdas, thickness, interference_order, r_index)
@ -197,53 +181,60 @@ def thickness_from_scheludko(wavelengths,
r_index_masked = r_index[mask] r_index_masked = r_index[mask]
intensities_masked = intensities[mask] intensities_masked = intensities[mask]
min_ecart = np.inf min_ecart = np.inf
best_m = None best_m = None
meilleure_h = None best_h = None
if plot: if plot:
plt.figure(figsize=(10, 6),dpi =600) plt.figure(figsize=(10, 6), dpi=300)
plt.ylabel(r'$h$ ($\mathrm{{nm}}$)') plt.ylabel(r'$h$ ($\mathrm{{nm}}$)')
plt.xlabel(r'$\lambda$ ($ \mathrm{nm} $)') plt.xlabel(r'$\lambda$ ($ \mathrm{nm} $)')
for m in range(0, max_tested_order+1): for m in range(0, max_tested_order+1):
h_values = thickness_scheludko_at_order(wavelengths_masked, intensities_masked, m, r_index_masked) h_values = thickness_scheludko_at_order(wavelengths_masked,
intensities_masked,
m, r_index_masked)
if plot: ecart = np.max(h_values) - np.min(h_values)
plt.plot(wavelengths_masked, h_values,'.', markersize=3, label=f"Épaisseur du film (Scheludko, m={m})")
ecart = np.max(h_values)-np.min(h_values)
print(f"Écart pour m={m} : {ecart:.3f} nm") print(f"Écart pour m={m} : {ecart:.3f} nm")
if ecart < min_ecart: if ecart < min_ecart:
min_ecart = ecart min_ecart = ecart
best_m = m best_m = m
meilleure_h = h_values best_h = h_values
DeltaVrai = (intensities_masked -np.min(intensities_masked))/(np.max(intensities_masked) -np.min(intensities_masked))
#DeltaVrai = (intensities_raw_masked -np.min(intensities_raw_masked))/(np.max(intensities_raw_masked) -np.min(intensities_raw_masked))
DeltaScheludko = Delta(wavelengths_masked, np.mean(meilleure_h), best_m, r_index_masked)
#print(np.mean(meilleure_h),np.std(meilleure_h))
if plot: if plot:
plt.figure(figsize=(10, 6), dpi=600) plt.plot(wavelengths_masked, h_values,'.', markersize=3, label=f"Épaisseur du film (Scheludko, m={m})")
plt.plot(wavelengths_masked, DeltaVrai,
'bo-', markersize=2, label=r'$\mathrm{{Smoothed}}\ \mathrm{{Data}}$')
plt.plot(wavelengths_masked, DeltaScheludko, # Delta
'go-', markersize=2, label = rf'$\mathrm{{Scheludko}}\ (h = {np.mean(meilleure_h):.1f} \pm {np.std(meilleure_h):.1f}\ \mathrm{{nm}})$') num = intensities_masked - np.min(intensities_masked)
denom = np.max(intensities_masked) - np.min(intensities_masked)
DeltaVrai = num / denom
# DeltaVrai = (intensities_masked -np.min(intensities_masked))/(np.max(intensities_masked) -np.min(intensities_masked))
# DeltaVrai = (intensities_raw_masked -np.min(intensities_raw_masked))/(np.max(intensities_raw_masked) -np.min(intensities_raw_masked))
DeltaScheludko = Delta(wavelengths_masked,
np.mean(best_h),
best_m,
r_index_masked)
xdata = (wavelengths_masked, r_index_masked) xdata = (wavelengths_masked, r_index_masked)
popt, pcov = curve_fit(lambda x, h: Delta_fit(x, h, m), xdata, DeltaVrai, p0=[np.mean(meilleure_h)]) popt, pcov = curve_fit(lambda x, h: Delta_fit(x, h, m), xdata, DeltaVrai, p0=[np.mean(best_h)])
fitted_h = popt[0] fitted_h = popt[0]
if plot: if plot:
plt.plot(wavelengths_masked, Delta(wavelengths_masked, fitted_h, best_m, r_index_masked ), 'ro-',markersize=2, label=rf'$\mathrm{{Fit}}\ (h = {fitted_h:.1f}\pm {np.sqrt(pcov[0][0]):.1f} \ \mathrm{{nm}})$') Delta_values = Delta(wavelengths_masked, fitted_h, best_m, r_index_masked)
plt.figure(figsize=(10, 6), dpi=300)
plt.plot(wavelengths_masked, DeltaVrai,
'bo-', markersize=2, label=r'$\mathrm{{Smoothed}}\ \mathrm{{Data}}$')
plt.plot(wavelengths_masked, DeltaScheludko,
'go-', markersize=2, label = rf'$\mathrm{{Scheludko}}\ (h = {np.mean(best_h):.1f} \pm {np.std(best_h):.1f}\ \mathrm{{nm}})$')
plt.plot(wavelengths_masked, Delta_values, 'ro-',markersize=2, label=rf'$\mathrm{{Fit}}\ (h = {fitted_h:.1f}\pm {np.sqrt(pcov[0][0]):.1f} \ \mathrm{{nm}})$')
plt.legend() plt.legend()
plt.ylabel(r'$\Delta$') plt.ylabel(r'$\Delta$')
plt.xlabel(r'$\lambda$ ($ \mathrm{nm} $)') plt.xlabel(r'$\lambda$ ($ \mathrm{nm} $)')
@ -258,8 +249,11 @@ def thickness_for_order0(wavelengths,
min_peak_prominence, min_peak_prominence,
plot=None): plot=None):
# TODO :
# Load "trou"
File_I_min = 'tests/spectre_trou/000043641.xy' File_I_min = 'tests/spectre_trou/000043641.xy'
wavelengths_I_min, intensities_I_min = load_spectrum(File_I_min, lambda_min=450)
r_index = refractive_index r_index = refractive_index
peaks_min, peaks_max = finds_peak(wavelengths, intensities, peaks_min, peaks_max = finds_peak(wavelengths, intensities,
@ -268,9 +262,6 @@ def thickness_for_order0(wavelengths,
wavelengths_I_min, intensities_I_min = load_spectrum(File_I_min, lambda_min=450)
lambda_unique = wavelengths[peaks_max[0]] lambda_unique = wavelengths[peaks_max[0]]
@ -281,9 +272,8 @@ def thickness_for_order0(wavelengths,
intensities_masked = intensities[mask] intensities_masked = intensities[mask]
intensities_I_min_masked =intensities_I_min[mask] intensities_I_min_masked =intensities_I_min[mask]
min_ecart = np.inf # best_m = None
best_m = None # best_h = None
meilleure_h = None
m = 0 m = 0
@ -294,37 +284,38 @@ def thickness_for_order0(wavelengths,
Imin=intensities_I_min_masked) Imin=intensities_I_min_masked)
if plot: if plot:
plt.figure(figsize=(10, 6), dpi=600) plt.figure(figsize=(10, 6), dpi=300)
plt.plot(wavelengths_masked, h_values, label=r"Épaisseur du film (Scheludko, m=0)") plt.plot(wavelengths_masked, h_values, label=r"Épaisseur du film (Scheludko, m=0)")
ecart = np.max(h_values) - np.min(h_values)
best_m = m best_m = m
meilleure_h = h_values best_h = h_values
# Delta
num = intensities_masked - np.min(intensities_I_min_masked)
denom = np.max(intensities_masked) - np.min(intensities_I_min_masked)
DeltaVrai = num / denom
DeltaScheludko = Delta(wavelengths_masked, np.mean(best_h), best_m, r_index_masked)
DeltaVrai = (intensities_masked -np.min(intensities_I_min_masked))/(np.max(intensities_masked) -np.min(intensities_I_min_masked)) #print(np.mean(best_h),np.std(best_h))
#DeltaVrai = (intensities_masked -np.min(intensities_masked))/(np.max(intensities_masked) -np.min(intensities_masked))
DeltaScheludko = Delta(wavelengths_masked, np.mean(meilleure_h), best_m, r_index_masked)
#print(np.mean(meilleure_h),np.std(meilleure_h))
if plot: if plot:
plt.figure(figsize=(10, 6), dpi=600) plt.figure(figsize=(10, 6), dpi=300)
plt.plot(wavelengths_masked,DeltaVrai,'bo-', markersize=2,label=r'$\mathrm{{Raw}}\ \mathrm{{Data}}$') plt.plot(wavelengths_masked,DeltaVrai,'bo-', markersize=2,label=r'$\mathrm{{Raw}}\ \mathrm{{Data}}$')
plt.plot(wavelengths_masked,DeltaScheludko,'ro-', markersize=2,label = rf'$\mathrm{{Scheludko}}\ (h = {np.mean(meilleure_h):.1f} \pm {np.std(meilleure_h):.1f}\ \mathrm{{nm}})$') plt.plot(wavelengths_masked,DeltaScheludko,'ro-', markersize=2,label = rf'$\mathrm{{Scheludko}}\ (h = {np.mean(best_h):.1f} \pm {np.std(best_h):.1f}\ \mathrm{{nm}})$')
xdata = (wavelengths_masked, r_index_masked) xdata = (wavelengths_masked, r_index_masked)
popt, pcov = curve_fit(lambda x, h: Delta_fit(x, h, m), xdata, DeltaVrai, p0=[np.mean(meilleure_h)]) popt, pcov = curve_fit(lambda x, h: Delta_fit(x, h, m), xdata, DeltaVrai, p0=[np.mean(best_h)])
fitted_h = popt[0] fitted_h = popt[0]
if plot: if plot:
plt.plot(wavelengths_masked, Delta(wavelengths_masked, fitted_h, best_m, r_index_masked ), 'go-',markersize=2, label=rf'$\mathrm{{Fit}}\ (h = {fitted_h:.1f}\pm {np.sqrt(pcov[0][0]):.1f} \ \mathrm{{nm}})$') Delta_values = Delta(wavelengths_masked, fitted_h, best_m, r_index_masked)
plt.plot(wavelengths_masked, Delta_values,
'go-', markersize=2,
label=rf'$\mathrm{{Fit}}\ (h = {fitted_h:.1f}\pm {np.sqrt(pcov[0][0]):.1f} \ \mathrm{{nm}})$')
plt.legend() plt.legend()
plt.ylabel(r'$\Delta$') plt.ylabel(r'$\Delta$')
plt.xlabel(r'$\lambda$ (nm)') plt.xlabel(r'$\lambda$ (nm)')
return OptimizeResult(thickness=fitted_h ,) return OptimizeResult(thickness=fitted_h,)