import pandas as pd

from scipy.signal import savgol_filter
from scipy.signal import find_peaks

import matplotlib.pyplot as plt
plt.rc('text', usetex=True)
plt.rcParams.update({
    'axes.labelsize': 26,
    'xtick.labelsize': 32,
    'ytick.labelsize': 32,
    'legend.fontsize': 23,
})

from .io import load_spectrum
from .fft import *
from .minmax import *





def plot_spectrum(lambdas, intensities, title=''):
    
    plt.figure(figsize=(10, 6),dpi =600)
    plt.plot(lambdas, intensities, 'o-', markersize=2)
    plt.xlabel(r'$\lambda$ (nm)')
    plt.ylabel(r'$I^*$')
    plt.title(title)
    plt.tight_layout() 
    plt.show()
    



def finds_peak(lambdas, intensities, min_peak_prominence, min_peak_distance=10, plot=None):
    """
    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.
    """
 

    peaks_max, _ = find_peaks(intensities, prominence=min_peak_prominence, distance=min_peak_distance)
    peaks_min, _ = find_peaks(-intensities, prominence=min_peak_prominence, distance=min_peak_distance)
    
    if plot:
        plt.figure(figsize=(10, 6),dpi =600)
        plt.plot(lambdas, intensities, 'o-', markersize=2, label="Smoothed data")
        plt.plot(lambdas[peaks_max], intensities[peaks_max], 'ro')
        plt.plot(lambdas[peaks_min], intensities[peaks_min], 'ro')
        plt.xlabel(r'$\lambda$ (nm)')
        plt.ylabel(r'$I^*$')
        plt.legend()
        plt.tight_layout() 
        plt.show()


    return peaks_min, peaks_max



def smooth_intensities(intensities):
    WIN_SIZE = 11
    smoothed_intensities = savgol_filter(intensities, WIN_SIZE, 3)
    return smoothed_intensities