analyse py graphique

Code/ldpc/src/analyse/ldpc_analysis_results.csv

@@ -0,0 +1,91 @@
+k,n,wc,wr,rate,p,ber,fer,avg_iter
+50,100,3,6,0.5000,0.000000,0.00000000,0.00000000,0.00
+50,100,3,6,0.5000,0.008571,0.09586000,0.21200000,0.00
+50,100,3,6,0.5000,0.017143,0.20772000,0.44500000,0.00
+50,100,3,6,0.5000,0.025714,0.28084000,0.58700000,0.00
+50,100,3,6,0.5000,0.034286,0.35496000,0.74100000,0.00
+50,100,3,6,0.5000,0.042857,0.39766000,0.82200000,0.00
+50,100,3,6,0.5000,0.051429,0.42240000,0.87200000,0.00
+50,100,3,6,0.5000,0.060000,0.44045000,0.91000000,0.00
+50,100,3,6,0.5000,0.068571,0.45518000,0.93500000,0.00
+50,100,3,6,0.5000,0.077143,0.47826000,0.97400000,0.00
+50,100,3,6,0.5000,0.085714,0.48009000,0.98200000,0.00
+50,100,3,6,0.5000,0.094286,0.48391000,0.98600000,0.00
+50,100,3,6,0.5000,0.102857,0.48988000,0.99300000,0.00
+50,100,3,6,0.5000,0.111429,0.49456000,0.99500000,0.00
+50,100,3,6,0.5000,0.120000,0.49429000,0.99700000,0.00
+50,75,3,9,0.6667,0.000000,0.00000000,0.00000000,0.00
+50,75,3,9,0.6667,0.008571,0.07490667,0.14800000,0.00
+50,75,3,9,0.6667,0.017143,0.17278667,0.34600000,0.00
+50,75,3,9,0.6667,0.025714,0.27240000,0.54800000,0.00
+50,75,3,9,0.6667,0.034286,0.34341333,0.69300000,0.00
+50,75,3,9,0.6667,0.042857,0.39881333,0.81500000,0.00
+50,75,3,9,0.6667,0.051429,0.43321333,0.88400000,0.00
+50,75,3,9,0.6667,0.060000,0.45101333,0.91400000,0.00
+50,75,3,9,0.6667,0.068571,0.47332000,0.95300000,0.00
+50,75,3,9,0.6667,0.077143,0.46954667,0.96000000,0.00
+50,75,3,9,0.6667,0.085714,0.47985333,0.98400000,0.00
+50,75,3,9,0.6667,0.094286,0.48470667,0.98600000,0.00
+50,75,3,9,0.6667,0.102857,0.49201333,0.99400000,0.00
+50,75,3,9,0.6667,0.111429,0.49064000,0.99400000,0.00
+50,75,3,9,0.6667,0.120000,0.49248000,0.99800000,0.00
+200,400,3,6,0.5000,0.000000,0.00000000,0.00000000,0.00
+200,400,3,6,0.5000,0.008571,0.30167250,0.60100000,0.00
+200,400,3,6,0.5000,0.017143,0.36523750,0.73200000,0.00
+200,400,3,6,0.5000,0.025714,0.43035000,0.86000000,0.00
+200,400,3,6,0.5000,0.034286,0.46297500,0.92400000,0.00
+200,400,3,6,0.5000,0.042857,0.49018000,0.98000000,0.00
+200,400,3,6,0.5000,0.051429,0.49391000,0.98900000,0.00
+200,400,3,6,0.5000,0.060000,0.49773500,0.99700000,0.00
+200,400,3,6,0.5000,0.068571,0.49869750,0.99700000,0.00
+200,400,3,6,0.5000,0.077143,0.50014500,1.00000000,50.00
+200,400,3,6,0.5000,0.085714,0.49800500,1.00000000,50.00
+200,400,3,6,0.5000,0.094286,0.50103250,1.00000000,50.00
+200,400,3,6,0.5000,0.102857,0.50059000,1.00000000,50.00
+200,400,3,6,0.5000,0.111429,0.49997500,1.00000000,50.00
+200,400,3,6,0.5000,0.120000,0.49996000,1.00000000,50.00
+200,300,3,9,0.6667,0.000000,0.00000000,0.00000000,0.00
+200,300,3,9,0.6667,0.008571,0.27226333,0.54500000,0.00
+200,300,3,9,0.6667,0.017143,0.38370000,0.77100000,0.00
+200,300,3,9,0.6667,0.025714,0.45723000,0.91600000,0.00
+200,300,3,9,0.6667,0.034286,0.48475000,0.97000000,0.00
+200,300,3,9,0.6667,0.042857,0.49562667,0.99200000,0.00
+200,300,3,9,0.6667,0.051429,0.49894000,0.99800000,0.00
+200,300,3,9,0.6667,0.060000,0.50088333,1.00000000,50.00
+200,300,3,9,0.6667,0.068571,0.49815667,1.00000000,50.00
+200,300,3,9,0.6667,0.077143,0.50034000,1.00000000,50.00
+200,300,3,9,0.6667,0.085714,0.49923333,1.00000000,50.00
+200,300,3,9,0.6667,0.094286,0.50140000,1.00000000,50.00
+200,300,3,9,0.6667,0.102857,0.49978333,1.00000000,50.00
+200,300,3,9,0.6667,0.111429,0.50114333,1.00000000,50.00
+200,300,3,9,0.6667,0.120000,0.50025667,1.00000000,50.00
+400,800,3,6,0.5000,0.000000,0.00000000,0.00000000,0.00
+400,800,3,6,0.5000,0.008571,0.32761250,0.65400000,0.00
+400,800,3,6,0.5000,0.017143,0.41412625,0.82000000,0.00
+400,800,3,6,0.5000,0.025714,0.46907375,0.92900000,0.00
+400,800,3,6,0.5000,0.034286,0.48519375,0.96900000,0.00
+400,800,3,6,0.5000,0.042857,0.49841750,0.99300000,0.00
+400,800,3,6,0.5000,0.051429,0.50101500,1.00000000,50.00
+400,800,3,6,0.5000,0.060000,0.49914250,1.00000000,50.00
+400,800,3,6,0.5000,0.068571,0.49955625,1.00000000,50.00
+400,800,3,6,0.5000,0.077143,0.49976500,1.00000000,50.00
+400,800,3,6,0.5000,0.085714,0.50057750,1.00000000,50.00
+400,800,3,6,0.5000,0.094286,0.50094625,1.00000000,50.00
+400,800,3,6,0.5000,0.102857,0.50099250,1.00000000,50.00
+400,800,3,6,0.5000,0.111429,0.50017125,1.00000000,50.00
+400,800,3,6,0.5000,0.120000,0.49992750,1.00000000,50.00
+400,600,3,9,0.6667,0.000000,0.00000000,0.00000000,0.00
+400,600,3,9,0.6667,0.008571,0.35087667,0.70300000,0.00
+400,600,3,9,0.6667,0.017143,0.43971000,0.88200000,0.00
+400,600,3,9,0.6667,0.025714,0.48824167,0.97500000,0.00
+400,600,3,9,0.6667,0.034286,0.49763833,0.99600000,0.00
+400,600,3,9,0.6667,0.042857,0.49956833,1.00000000,50.00
+400,600,3,9,0.6667,0.051429,0.49943667,1.00000000,50.00
+400,600,3,9,0.6667,0.060000,0.50025667,1.00000000,50.00
+400,600,3,9,0.6667,0.068571,0.49899333,1.00000000,50.00
+400,600,3,9,0.6667,0.077143,0.49918333,1.00000000,50.00
+400,600,3,9,0.6667,0.085714,0.50041833,1.00000000,50.00
+400,600,3,9,0.6667,0.094286,0.49998167,1.00000000,50.00
+400,600,3,9,0.6667,0.102857,0.50129000,1.00000000,50.00
+400,600,3,9,0.6667,0.111429,0.49973500,1.00000000,50.00
+400,600,3,9,0.6667,0.120000,0.50078833,1.00000000,50.00

Code/ldpc/src/analyse/trace.py

@@ -0,0 +1,138 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+import sys
+from matplotlib.colors import LogNorm
+import matplotlib.ticker as ticker
+
+sns.set_theme(style="whitegrid", context="talk")
+plt.rcParams['font.family'] = 'sans-serif'
+plt.rcParams['figure.dpi'] = 300
+
+def load_data(filename):
+    try:
+        df = pd.read_csv(filename)
+        
+        df['Label'] = (
+            "k=" + df['k'].astype(str) + 
+            " | Wc=" + df['wc'].astype(str) + 
+            " | Wr=" + df['wr'].astype(str)
+        )
+        
+        df = df.sort_values(by=['k', 'wc', 'wr'])
+        
+        return df
+    except FileNotFoundError:
+        print(f"Erreur : Le fichier '{filename}' est introuvable.")
+        sys.exit(1)
+
+def plot_1_waterfall_zoomed(df):
+    print("   -> Génération du Waterfall...")
+    plt.figure(figsize=(14, 9))
+    
+    df_zoom = df[df['ber'] > 0].copy()
+    
+    if df_zoom.empty:
+        print("    Pas d'erreurs trouvées, impossible de tracer.")
+        return
+
+    sns.lineplot(
+        data=df_zoom, 
+        x='p', 
+        y='ber', 
+        hue='Label', 
+        style='Label',
+        markers=True, 
+        dashes=False, 
+        linewidth=2.5, 
+        palette="turbo",
+        markersize=9
+    )
+
+    plt.yscale('log')
+    ax = plt.gca()
+    ax.yaxis.set_major_locator(ticker.LogLocator(base=10.0, numticks=12))
+    
+    plt.title("Vue Globale : Performance des configurations", fontweight='bold', pad=20)
+    plt.ylabel("BER [Log Scale]", labelpad=15)
+    plt.xlabel("Bruit du Canal (p)", labelpad=15)
+    plt.grid(True, which="both", ls="-", alpha=0.3)
+    
+    plt.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0, title="Configuration", fontsize='x-small')
+    
+    plt.tight_layout()
+    plt.savefig("Final_1_Waterfall.png")
+
+def plot_2_ranking_bars(df):
+    print("   -> Génération du Classement...")
+    
+    means = df.groupby('p')['ber'].mean()
+    target_p = means[means > 0.0001].first_valid_index()
+    
+    if target_p is None:
+        target_p = df['p'].max()
+
+    subset = df[df['p'] == target_p].sort_values('ber', ascending=True)
+
+    plt.figure(figsize=(12, max(6, len(subset)*0.4))) 
+    
+    barplot = sns.barplot(
+        data=subset, 
+        y='Label', 
+        x='ber', 
+        palette="viridis", 
+        edgecolor=".2"
+    )
+    
+    plt.xscale('log')
+    plt.title(f"Robustesse p={target_p:.3f} (Gauche = Meilleur)", fontweight='bold', pad=20)
+    plt.xlabel("BER", labelpad=10)
+    plt.ylabel("") 
+    
+    for i, container in enumerate(barplot.containers):
+        barplot.bar_label(container, fmt='%.1e', padding=5, fontsize=11)
+        
+    plt.grid(True, axis='x', which="both", ls="--", alpha=0.5)
+    plt.tight_layout()
+    plt.savefig("Final_2_Classement.png")
+
+def plot_3_heatmap_gradient(df):
+    print("   -> Génération de la Heatmap...")
+    
+    pivot = df.pivot(index="Label", columns="p", values="ber")
+    
+    pivot_log = pivot.replace(0, 1e-9) 
+    
+    plt.figure(figsize=(16, max(6, len(pivot)*0.5))) 
+    
+    ax = sns.heatmap(
+        pivot_log, 
+        cmap="Spectral_r", 
+        norm=LogNorm(vmin=1e-5, vmax=1),
+        cbar_kws={'label': 'BER (Log Scale)'},
+        linewidths=0.5,
+        linecolor='white'
+    )
+    
+    plt.title("Résistance (Bleu=Zone de Confort, Rouge=Zone d'Échec)", fontweight='bold', pad=20)
+    plt.xlabel("Bruit du Canal (p)", labelpad=10)
+    plt.ylabel("")
+    
+    plt.xticks(rotation=45)
+    plt.tight_layout()
+    plt.savefig("Final_3_Heatmap.png")
+
+if __name__ == "__main__":
+    print("Démarrage de l'analyse graphique finale...")
+    
+    df = load_data("ldpc_analysis_results.csv")
+    
+    plot_1_waterfall_zoomed(df)
+    plot_2_ranking_bars(df)
+    plot_3_heatmap_gradient(df)
+    
+    print("\nTerminé ! Trois images générées :")
+    print("   1. Final_1_Waterfall.png (Courbes détaillées)")
+    print("   2. Final_2_Classement.png (Comparatif barres)")
+    print("   3. Final_3_Heatmap.png (Vision globale couleur)")