final commit

2026-06-19 11:15:39 +02:00
parent e2b1b20913
commit d095840037
975 changed files with 1232523 additions and 0 deletions
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+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()