import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from scipy.interpolate import make_interp_spline, BSpline

# === Chargement des données ===
chemin_fichier = "data.xlsx"
df = pd.read_excel(chemin_fichier)

# === Filtrer les sources ===
df = df[df["type"].str.lower() == "source"]
df = df.dropna(subset=["c25°C ", "Nom"])

# === Couleurs par source ===
couleurs_sources = {
    "Bullac": "#D77D00",
    "Corn": "#A1C935",
    "Bual": "#F5D200",
    "La diège": "#9271EA",
    "Ayrissac": "#EB64C3",
    "Pito": "#D52117",
    "Ressel": "#1DC6C3",
    "Marchepied": "#3FB94D",
    "Anglades": "#0084C9",
    "Liauzu": "#431D84",
    "Pescalerie": "#D8676B",
    "Sagne": "tab:purple",
}

# === Calcul des fréquences normalisées ===
freq_absolue = df.groupby(["Nom", "c25°C "]).size().reset_index(name="frequence")
freq_totale = freq_absolue.groupby("Nom")["frequence"].transform("sum")
freq_absolue["frequence_normalisee"] = freq_absolue["frequence"] / freq_totale

# === Sources à afficher dans l'ordre souhaité ===
noms_ordonnes = [
    "Sagne", "Ressel", "Pito", "Pescalerie", "Marchepied",
    "Liauzu", "La diège", "Corn", "Bullac", "Bual", "Ayrissac", "Anglades"
]
sources_disponibles = set(freq_absolue["Nom"].unique())
noms_valides = [nom for nom in noms_ordonnes if nom in sources_disponibles]

# === Graphique 3D ===
fig = plt.figure(figsize=(20, 12))
ax = fig.add_subplot(111, projection='3d')

y_spacing = 4.0

for idx, nom in enumerate(reversed(noms_valides)):
    df_nom = freq_absolue[freq_absolue["Nom"] == nom].sort_values("c25°C ")
    if df_nom.empty:
        continue

    xs = df_nom["c25°C "].to_numpy()
    ys = df_nom["frequence_normalisee"].to_numpy()
    color = couleurs_sources.get(nom, "gray")
    y_val = (len(noms_valides) - 1 - idx) * y_spacing

    # === Courbe type histogramme ===
    x_curve = []
    y_curve = []

    for i in range(len(xs)):
        x_curve.extend([xs[i] - 0.5, xs[i], xs[i] + 0.5])
        y_curve.extend([0, ys[i], 0])

        # Relier à la suivante si proche
        if i < len(xs) - 1 and xs[i + 1] - xs[i] <= 1.5:
            x_curve.extend([xs[i] + 0.5, xs[i + 1] - 0.5])
            y_curve.extend([0, 0])

    # X_Y_Spline = make_interp_spline(x_curve, y_curve)
    # X_ = np.linspace(x_curve.min(), x_curve.max(), 500)
    # Y_ = X_Y_Spline(X_)
    
    # ax.plot(X_, [y_val] * len(Y_), Y_, color=color, linewidth=2, alpha=0.8)
    
    res = {key: value for key, value in zip(x_curve, y_curve)}
    x_curve = list(res.keys())
    y_curve = list(res.values())
        
    # Interpolation pour lisser la courbe
    spline = make_interp_spline(x_curve, y_curve, k=1)
    x_curve = np.linspace(min(x_curve), max(x_curve), 100)
    y_curve = spline(x_curve)
    
    
    ax.plot(x_curve, [y_val] * len(x_curve), y_curve, color=color, linewidth=2, alpha=0.8)
   
    # Nom de la source à gauche
    ax.text(
        x=freq_absolue["c25°C "].min() - 30,
        y=y_val,
        z=0,
        s=nom,
        color=color,
        fontsize=10,
        ha='right',
        va='center'
    )

# === Configuration du graphique ===
ax.set_yticks([])
ax.set_xlabel("Conductivité (c25°C, µS/cm)", labelpad=15)
ax.set_ylabel("")
ax.set_zlabel("Fréquence normalisée", labelpad=10)
ax.set_title("Courbes 3D type histogrammes par source", pad=20)

ax.set_xlim(freq_absolue["c25°C "].min() - 35, freq_absolue["c25°C "].max() + 10)
ax.set_ylim(-y_spacing, len(noms_valides) * y_spacing)
ax.set_zlim(0, 0.4)
ax.view_init(elev=25, azim=-140)
# ax.autoscale()
plt.tight_layout()
plt.show()