fr/tmp_optifik
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

simplify finds_peak

Browse source
This commit is contained in:
François Boulogne 2025-05-21 14:05:08 +02:00
parent 022966608a
commit 56aac01151
6 changed files with 66 additions and 88 deletions
Download patch file Download diff file Expand all files Collapse all files

15
check.py
View file

@ -61,13 +61,16 @@ def check_SV1():
spectre_file = os.path.join(DATA_FOLDER, fn) spectre_file = os.path.join(DATA_FOLDER, fn)
##### Affichage du spectre brut et récupération des Intesités brutes##### lambdas, raw_intensities = load_spectrum(spectre_file, lambda_min=450)
raw_intensities = plot_xy(spectre_file)
##### Affichage du spectre lissé ##### ##### Affichage du spectre lissé #####
#smoothed_intensities, intensities, lambdas = Data_Smoothed(spectre_file)
smoothed_intensities = smooth_intensities(raw_intensities)
smoothed_intensities, intensities, lambdas = Data_Smoothed(spectre_file)
# smoothed_intensities, intensities, lambdas = Data_Smoothed(spectre_file)
##### Indice Optique en fonction de Lambda ##### ##### Indice Optique en fonction de Lambda #####
@ -97,4 +100,4 @@ def check_SV1():
if __name__ == '__main__': if __name__ == '__main__':
check_basic() check_basic()
#check_SV1() check_SV1()

53
optifik/analysis.py
View file

@ -48,14 +48,7 @@ def plot_xy(file_path, plot=True):
def finds_peak(lambdas, intensities, min_peak_prominence, min_peak_distance=10, plot=None):
def finds_peak(filename, min_peak_prominence, min_peak_distance=10, plot=None):
""" """
Charge un fichier .xy et affiche les données avec les extrema détectés (minima et maxima). Charge un fichier .xy et affiche les données avec les extrema détectés (minima et maxima).
@ -68,10 +61,11 @@ def finds_peak(filename, min_peak_prominence, min_peak_distance=10, plot=None):
min_peak_distance : float min_peak_distance : float
Distance minimale entre les pics. Distance minimale entre les pics.
""" """
# Charger et lisser les données
lambdas, intensities = load_spectrum(filename, lambda_min=450) smoothed_intensities = intensities
WIN_SIZE = 11
smoothed_intensities = savgol_filter(intensities, WIN_SIZE, 3)
# Trouver les maxima et minima sur le signal lissé # Trouver les maxima et minima sur le signal lissé
peaks_max, _ = find_peaks(smoothed_intensities, prominence=min_peak_prominence, distance=min_peak_distance) peaks_max, _ = find_peaks(smoothed_intensities, prominence=min_peak_prominence, distance=min_peak_distance)
peaks_min, _ = find_peaks(-smoothed_intensities, prominence=min_peak_prominence, distance=min_peak_distance) peaks_min, _ = find_peaks(-smoothed_intensities, prominence=min_peak_prominence, distance=min_peak_distance)
@ -84,50 +78,29 @@ def finds_peak(filename, min_peak_prominence, min_peak_distance=10, plot=None):
plt.xlabel(r'$\lambda$ (nm)') plt.xlabel(r'$\lambda$ (nm)')
plt.ylabel(r'$I^*$') plt.ylabel(r'$I^*$')
plt.legend() plt.legend()
plt.tight_layout() plt.tight_layout()
plt.show() plt.show()
# Nombre total d’extremums # Nombre total d’extremums
total_extrema = len(peaks_max) + len(peaks_min) total_extrema = len(peaks_max) + len(peaks_min)
if total_extrema >=15: if total_extrema >= 15:
print('Number of extrema', total_extrema,'.') print('Number of extrema', total_extrema,'.')
print('FFT method') print('FFT method')
if total_extrema <=15 and total_extrema >4: if total_extrema <= 15 and total_extrema > 4:
print('Number of extrema', total_extrema,'.') print('Number of extrema', total_extrema,'.')
print('OOSpectro method') print('OOSpectro method')
if total_extrema <=4: if total_extrema <= 4:
print('Number of extrema', total_extrema,'.') print('Number of extrema', total_extrema,'.')
print('Scheludko method') print('Scheludko method')
return total_extrema, smoothed_intensities, intensities, lambdas, peaks_min, peaks_max return total_extrema, peaks_min, peaks_max
def smooth_intensities(intensities):
def Data_Smoothed(filename):
"""
Charge un fichier .xy et affiche les données avec les extrema détectés (minima et maxima).
Parameters
----------
filename : str
Chemin vers le fichier .xy (2 colonnes : lambda et intensité).
min_peak_prominence : float
Importance minimale des pics.
min_peak_distance : float
Distance minimale entre les pics.
"""
# Charger et lisser les données
lambdas, intensities = load_spectrum(filename, lambda_min=450)
WIN_SIZE = 11 WIN_SIZE = 11
smoothed_intensities = savgol_filter(intensities, WIN_SIZE, 3) smoothed_intensities = savgol_filter(intensities, WIN_SIZE, 3)
return smoothed_intensities
return smoothed_intensities, intensities, lambdas

12
optifik/auto.py
View file

@ -10,11 +10,13 @@ def auto(DATA_FOLDER, FILE_NAME, plot=None):
##### Affichage du spectre brut et récupération des Intesités brutes##### ##### Affichage du spectre brut et récupération des Intesités brutes#####
raw_intensities = load_spectrum(spectre_file) lambdas, raw_intensities = load_spectrum(spectre_file, lambda_min=450)
##### Affichage du spectre lissé ##### ##### Affichage du spectre lissé #####
smoothed_intensities, intensities, lambdas = Data_Smoothed(spectre_file) #smoothed_intensities, intensities, lambdas = Data_Smoothed(spectre_file)
smoothed_intensities = smooth_intensities(raw_intensities)
##### Indice Optique en fonction de Lambda ##### ##### Indice Optique en fonction de Lambda #####
@ -30,9 +32,9 @@ def auto(DATA_FOLDER, FILE_NAME, plot=None):
prominence = 0.03 prominence = 0.03
##### Find Peak ##### ##### Find Peak #####
total_extrema, smoothed_intensities, raw_intensities, lambdas, peaks_min, peaks_max = finds_peak(spectre_file, total_extrema, peaks_min, peaks_max = finds_peak(lambdas, smoothed_intensities,
min_peak_prominence=prominence, min_peak_prominence=prominence,
plot=plot) plot=plot)
##### Epaisseur selon la methode ##### ##### Epaisseur selon la methode #####

18
tests/test_SV1.py
View file

@ -4,9 +4,9 @@ from numpy.testing import assert_allclose
import pytest import pytest
from optifik.minmax import thickness_from_minmax from optifik.minmax import thickness_from_minmax
from optifik.analysis import Data_Smoothed
from optifik.analysis import finds_peak
from optifik.io import load_spectrum from optifik.io import load_spectrum
from optifik.analysis import smooth_intensities
from optifik.analysis import finds_peak
import yaml import yaml
@ -21,17 +21,17 @@ def load():
return data return data
@pytest.mark.parametrize("spectrum, expected", load()) @pytest.mark.parametrize("spectrum_path, expected", load())
def test_minmax(spectrum, expected): def test_minmax(spectrum_path, expected):
raw_intensities = load_spectrum(spectrum) lambdas, raw_intensities = load_spectrum(spectrum_path, lambda_min=450)
smoothed_intensities = smooth_intensities(raw_intensities)
smoothed_intensities, intensities, lambdas = Data_Smoothed(spectrum)
indice = 1.324188 + 3102.060378 / (lambdas**2) indice = 1.324188 + 3102.060378 / (lambdas**2)
prominence = 0.02 prominence = 0.02
total_extrema, smoothed_intensities, raw_intensities, lambdas, peaks_min, peaks_max = finds_peak(spectrum, total_extrema, peaks_min, peaks_max = finds_peak(lambdas, smoothed_intensities,
min_peak_prominence=prominence) min_peak_prominence=prominence,
plot=False)
thickness_minmax = thickness_from_minmax(lambdas, thickness_minmax = thickness_from_minmax(lambdas,
smoothed_intensities, smoothed_intensities,

16
tests/test_SV2o5.py
View file

@ -5,7 +5,7 @@ import pytest
from optifik.scheludko import thickness_from_scheludko from optifik.scheludko import thickness_from_scheludko
from optifik.io import load_spectrum from optifik.io import load_spectrum
from optifik.analysis import Data_Smoothed from optifik.analysis import smooth_intensities
from optifik.analysis import finds_peak from optifik.analysis import finds_peak
import yaml import yaml
@ -21,17 +21,17 @@ def load():
return data return data
@pytest.mark.parametrize("spectrum, expected", load()) @pytest.mark.parametrize("spectrum_path, expected", load())
def test_minmax(spectrum, expected): def test_minmax(spectrum_path, expected):
raw_intensities = load_spectrum(spectrum) lambdas, raw_intensities = load_spectrum(spectrum_path, lambda_min=450)
smoothed_intensities = smooth_intensities(raw_intensities)
smoothed_intensities, intensities, lambdas = Data_Smoothed(spectrum)
refractive_index = 1.324188 + 3102.060378 / (lambdas**2) refractive_index = 1.324188 + 3102.060378 / (lambdas**2)
prominence = 0.02 prominence = 0.02
total_extrema, smoothed_intensities, raw_intensities, lambdas, peaks_min, peaks_max = finds_peak(spectrum, total_extrema, peaks_min, peaks_max = finds_peak(lambdas, smoothed_intensities,
min_peak_prominence=prominence) min_peak_prominence=prominence,
plot=False)
thickness_scheludko = thickness_from_scheludko(lambdas, thickness_scheludko = thickness_from_scheludko(lambdas,
smoothed_intensities, smoothed_intensities,

40
tests/test_basic.py
View file

@ -7,8 +7,7 @@ from optifik.analysis import thickness_from_fft
from optifik.analysis import thickness_from_minmax from optifik.analysis import thickness_from_minmax
from optifik.analysis import thickness_from_scheludko from optifik.analysis import thickness_from_scheludko
from optifik.analysis import thickness_for_order0 from optifik.analysis import thickness_for_order0
from optifik.analysis import plot_xy from optifik.analysis import smooth_intensities
from optifik.analysis import Data_Smoothed
from optifik.analysis import Prominence_from_fft from optifik.analysis import Prominence_from_fft
from optifik.analysis import finds_peak from optifik.analysis import finds_peak
from optifik.io import load_spectrum from optifik.io import load_spectrum
@ -20,8 +19,8 @@ def test_FFT():
expected = 3524.51 expected = 3524.51
spectrum_path = os.path.join(FOLDER, FILE_NAME) spectrum_path = os.path.join(FOLDER, FILE_NAME)
raw_intensities = load_spectrum(spectrum_path) lambdas, raw_intensities = load_spectrum(spectrum_path, lambda_min=450)
smoothed_intensities, intensities, lambdas = Data_Smoothed(spectrum_path) smoothed_intensities = smooth_intensities(raw_intensities)
indice = 1.324188 + 3102.060378 / (lambdas**2) indice = 1.324188 + 3102.060378 / (lambdas**2)
thickness_FFT = thickness_from_fft(lambdas, thickness_FFT = thickness_from_fft(lambdas,
@ -37,8 +36,8 @@ def test_minmax_ransac():
expected = 1338.35 expected = 1338.35
spectrum_path = os.path.join(FOLDER, FILE_NAME) spectrum_path = os.path.join(FOLDER, FILE_NAME)
raw_intensities = load_spectrum(spectrum_path) lambdas, raw_intensities = load_spectrum(spectrum_path, lambda_min=450)
smoothed_intensities, intensities, lambdas = Data_Smoothed(spectrum_path) smoothed_intensities = smooth_intensities(raw_intensities)
indice = 1.324188 + 3102.060378 / (lambdas**2) indice = 1.324188 + 3102.060378 / (lambdas**2)
prominence = Prominence_from_fft(lambdas=lambdas, prominence = Prominence_from_fft(lambdas=lambdas,
@ -64,14 +63,15 @@ def test_scheludko_4peaks():
expected = 777.07 expected = 777.07
spectrum_path = os.path.join(FOLDER, FILE_NAME) spectrum_path = os.path.join(FOLDER, FILE_NAME)
raw_intensities = load_spectrum(spectrum_path) lambdas, raw_intensities = load_spectrum(spectrum_path, lambda_min=450)
smoothed_intensities, intensities, lambdas = Data_Smoothed(spectrum_path) smoothed_intensities = smooth_intensities(raw_intensities)
indice = 1.324188 + 3102.060378 / (lambdas**2) indice = 1.324188 + 3102.060378 / (lambdas**2)
prominence = Prominence_from_fft(lambdas=lambdas, intensities=smoothed_intensities, refractive_index=indice, plot=False) prominence = Prominence_from_fft(lambdas=lambdas, intensities=smoothed_intensities, refractive_index=indice, plot=False)
total_extrema, smoothed_intensities, raw_intensities, lambdas, peaks_min, peaks_max = finds_peak(spectrum_path, total_extrema, peaks_min, peaks_max = finds_peak(lambdas, smoothed_intensities,
min_peak_prominence=prominence) min_peak_prominence=prominence,
plot=False)
result = thickness_from_scheludko(lambdas, smoothed_intensities, result = thickness_from_scheludko(lambdas, smoothed_intensities,
@ -89,15 +89,15 @@ def test_scheludko_2peaks():
expected = 495.69 expected = 495.69
spectrum_path = os.path.join(FOLDER, FILE_NAME) spectrum_path = os.path.join(FOLDER, FILE_NAME)
raw_intensities = load_spectrum(spectrum_path) lambdas, raw_intensities = load_spectrum(spectrum_path, lambda_min=450)
smoothed_intensities, intensities, lambdas = Data_Smoothed(spectrum_path) smoothed_intensities = smooth_intensities(raw_intensities)
indice = 1.324188 + 3102.060378 / (lambdas**2) indice = 1.324188 + 3102.060378 / (lambdas**2)
prominence = 0.03 prominence = 0.03
total_extrema, smoothed_intensities, raw_intensities, lambdas, peaks_min, peaks_max = finds_peak(spectrum_path, total_extrema, peaks_min, peaks_max = finds_peak(lambdas, smoothed_intensities,
min_peak_prominence=prominence) min_peak_prominence=prominence,
plot=False)
result = thickness_from_scheludko(lambdas, smoothed_intensities, result = thickness_from_scheludko(lambdas, smoothed_intensities,
peaks_min, peaks_max, peaks_min, peaks_max,
@ -116,15 +116,15 @@ def test_order0():
expected = 115.33 expected = 115.33
spectrum_path = os.path.join(FOLDER, FILE_NAME) spectrum_path = os.path.join(FOLDER, FILE_NAME)
raw_intensities = load_spectrum(spectrum_path) lambdas, raw_intensities = load_spectrum(spectrum_path, lambda_min=450)
smoothed_intensities, intensities, lambdas = Data_Smoothed(spectrum_path) smoothed_intensities = smooth_intensities(raw_intensities)
indice = 1.324188 + 3102.060378 / (lambdas**2) indice = 1.324188 + 3102.060378 / (lambdas**2)
prominence = 0.03 prominence = 0.03
total_extrema, smoothed_intensities, raw_intensities, lambdas, peaks_min, peaks_max = finds_peak(spectrum_path, total_extrema, peaks_min, peaks_max = finds_peak(lambdas, smoothed_intensities,
min_peak_prominence=prominence) min_peak_prominence=prominence,
plot=False)
result = thickness_for_order0(lambdas, smoothed_intensities, result = thickness_for_order0(lambdas, smoothed_intensities,
peaks_min, peaks_max, peaks_min, peaks_max,