75 lines
2 KiB
Python
75 lines
2 KiB
Python
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import pandas as pd
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from scipy.signal import savgol_filter
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from scipy.signal import find_peaks
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import matplotlib.pyplot as plt
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plt.rc('text', usetex=True)
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plt.rcParams.update({
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'axes.labelsize': 26,
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'xtick.labelsize': 32,
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'ytick.labelsize': 32,
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'legend.fontsize': 23,
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})
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from .io import load_spectrum
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from .fft import *
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from .minmax import *
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def plot_spectrum(lambdas, intensities, title=''):
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plt.figure(figsize=(10, 6),dpi =600)
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plt.plot(lambdas, intensities, 'o-', markersize=2)
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plt.xlabel(r'$\lambda$ (nm)')
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plt.ylabel(r'$I^*$')
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plt.title(title)
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plt.tight_layout()
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plt.show()
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def finds_peak(lambdas, intensities, min_peak_prominence, min_peak_distance=10, plot=None):
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"""
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Charge un fichier .xy et affiche les données avec les extrema détectés (minima et maxima).
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Parameters
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----------
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filename : str
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Chemin vers le fichier .xy (2 colonnes : lambda et intensité).
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min_peak_prominence : float
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Importance minimale des pics.
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min_peak_distance : float
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Distance minimale entre les pics.
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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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if plot:
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plt.figure(figsize=(10, 6),dpi =600)
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plt.plot(lambdas, intensities, 'o-', markersize=2, label="Smoothed data")
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plt.plot(lambdas[peaks_max], intensities[peaks_max], 'ro')
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plt.plot(lambdas[peaks_min], intensities[peaks_min], 'ro')
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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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plt.tight_layout()
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plt.show()
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return peaks_min, peaks_max
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def smooth_intensities(intensities):
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WIN_SIZE = 11
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smoothed_intensities = savgol_filter(intensities, WIN_SIZE, 3)
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return smoothed_intensities
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