Update GitHub Artifact Actions to v6 (major)

.github/workflows/code-coverage.yml

@@ -32,7 +32,7 @@ jobs:
           coverage xml -o coverage.xml
 
       - name: "Save coverage report as artifact"
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v6
         with:
           name: coverage-report
           path: coverage.xml

.github/workflows/rossler_demo.yml

@@ -95,7 +95,7 @@ jobs:
 
     # ---------- Upload validation artefacts ----------
     - name: Upload validation artefacts
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v6
       with:
         name: surrogate-validation
         path: |

.github/workflows/scorecard.yml

@@ -42,7 +42,7 @@ jobs:
       # Upload the results as artifacts (optional). Commenting out will disable
       # uploads of run results in SARIF format to the repository Actions tab.
       - name: "Upload artifact"
-        uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
+        uses: actions/upload-artifact@b7c566a772e6b6bfb58ed0dc250532a479d7789f # v6.0.0
         with:
           name: SARIF file
           path: results.sarif

SMdRQA/RQA2.py

@@ -546,7 +546,7 @@
     def load_results(self, filepath):
         """Load pre-computed RQA results from file."""
         with open(filepath, 'rb') as f:
             results = pickle.load(f)
 
         self.data = results['data']
         self._tau = results['tau']
@@ -1309,15 +1309,15 @@
         with ProcessPoolExecutor(max_workers=self.max_workers) as executor:
             futures = [
                 executor.submit(
-                    self._generate_with_seed, 
+                    self._generate_with_seed,
                     kind, seed, **kwargs
                 )
                 for seed in seeds
             ]
             for future in as_completed(futures):
                 surrogates.append(future.result())
         return np.vstack(surrogates)
-        
+
     def _generate_with_seed(
         self,
         kind: Algorithm,
@@ -1331,7 +1331,7 @@
             seed=seed,
             max_workers=1  # Disable nested parallelism
         )
-    
+
         # Dispatch to appropriate surrogate method
         method_name = f"_{kind.lower()}"
         method = getattr(temp_tests, method_name)

docs/examples/rossler_attractor_analysis.py

@@ -3,18 +3,18 @@
 # rossler_attractor_analysis.py - Corrected version based on actual RQA2.py implementation
 # Set up matplotlib for non-interactive use
 
+from tqdm import tqdm
+from SMdRQA.RQA2 import RQA2, RQA2_simulators, RQA2_tests
+import seaborn as sns
+import os
+from mpl_toolkits.mplot3d import Axes3D
+import matplotlib.pyplot as plt
+import numpy as np
 import matplotlib
 matplotlib.use('Agg')  # Use non-interactive backend
 
-import numpy as np
-import matplotlib.pyplot as plt
-from mpl_toolkits.mplot3d import Axes3D
-import os
-import seaborn as sns
 
 # Import from the actual RQA2 module structure
-from SMdRQA.RQA2 import RQA2, RQA2_simulators, RQA2_tests
-from tqdm import tqdm
 
 print("Starting Rössler Attractor Analysis...")
 
@@ -42,16 +42,22 @@
 # Chaotic attractor
 ax1 = fig.add_subplot(121, projection='3d')
 ax1.plot(x_chaos, y_chaos, z_chaos, c='maroon', lw=0.5, alpha=0.7)
-ax1.set_title(f"Chaotic Rössler Attractor (a={A_CHAOS})", fontweight='bold', fontsize=14)
+ax1.set_title(
+    f"Chaotic Rössler Attractor (a={A_CHAOS})",
+    fontweight='bold',
+    fontsize=14)
 ax1.set_xlabel("X", fontsize=12)
-ax1.set_ylabel("Y", fontsize=12) 
+ax1.set_ylabel("Y", fontsize=12)
 ax1.set_zlabel("Z", fontsize=12)
 ax1.view_init(30, -60)
 
 # Periodic attractor
 ax2 = fig.add_subplot(122, projection='3d')
 ax2.plot(x_per, y_per, z_per, c='teal', lw=0.5, alpha=0.7)
-ax2.set_title(f"Periodic Rössler Attractor (a={A_PER})", fontweight='bold', fontsize=14)
+ax2.set_title(
+    f"Periodic Rössler Attractor (a={A_PER})",
+    fontweight='bold',
+    fontsize=14)
 ax2.set_xlabel("X", fontsize=12)
 ax2.set_ylabel("Y", fontsize=12)
 ax2.set_zlabel("Z", fontsize=12)
@@ -67,17 +73,18 @@
 methods = ["FT", "AAFT", "IAAFT", "IDFS", "WIAAFT", "PPS"]
 metrics = ["lyapunov_exponent", "time_irreversibility"]
 
+
 def compute_surrogate_metrics(signal, method):
     """Compute metrics for original signal and its surrogates."""
     print(f"    Processing {method} surrogates...")
     tester = RQA2_tests(signal, seed=123, max_workers=2)
-    
+
     # Generate surrogates
     surrogates = tester.generate(kind=method, n_surrogates=N_SURR)
-    
+
     # Compute metrics for original signal
     orig_metrics = tester._calculate_all_metrics(signal)
-    
+
     # Compute metrics for all surrogates
     surr_metrics = {m: [] for m in metrics}
     for i, s in tqdm(enumerate(surrogates)):
@@ -87,17 +94,19 @@ def compute_surrogate_metrics(signal, method):
         for m in metrics:
             surr_metrics[m].append(s_vals[m])
             print('Metric value:', s_vals[m])
-    
+
     return orig_metrics, surr_metrics
 
+
 # Compute surrogate metrics for both regimes
 results = {}
 print("\nStarting surrogate analysis...")
 
 for regime, signal in [("Chaotic", x_chaos), ("Periodic", x_per)]:
     results[regime] = {}
-    print(f"\nProcessing {regime} regime (a={'0.1' if regime=='Chaotic' else '0.3'})...")
-
+    print(
+        f"\nProcessing {regime} regime (a={'0.1' if regime=='Chaotic' else '0.3'})...")
+
     for method in methods:
         try:
             orig, surr = compute_surrogate_metrics(signal, method)
@@ -117,15 +126,15 @@ def compute_surrogate_metrics(signal, method):
 for r, regime in enumerate(["Chaotic", "Periodic"]):
     for m, metric in enumerate(metrics):
         ax = axes[r, m]
-        
+
         # Plot histograms for each surrogate method
         for i, method in enumerate(methods):
             if method in results[regime]:
                 orig_val, surr_data = results[regime][method]
-                
+
                 # Filter out NaN values
                 valid_surr = [x for x in surr_data[metric] if not np.isnan(x)]
-                
+
                 if valid_surr:
                     sns.kdeplot(data=np.log(valid_surr),
                                 ax=ax,
@@ -134,23 +143,25 @@ def compute_surrogate_metrics(signal, method):
                                 alpha=0.6,            # transparency
                                 color=colors[i % len(colors)],
                                 linewidth=2)
-                    
+
                     # Plot original value as vertical line
                     if not np.isnan(orig_val[metric]):
-                        ax.axvline(np.log(orig_val[metric]), color=colors[i % len(colors)], 
-                                 linestyle='--', linewidth=2, alpha=0.8)
-        
-        ax.set_title(f"{regime}: {metric.replace('_', ' ').title()}", 
-                    fontweight='bold', fontsize=12)
+                        ax.axvline(np.log(orig_val[metric]), color=colors[i % len(
+                            colors)], linestyle='--', linewidth=2, alpha=0.8)
+
+        ax.set_title(f"{regime}: {metric.replace('_', ' ').title()}",
+                     fontweight='bold', fontsize=12)
         ax.set_xlabel("Log Value", fontsize=10)
         ax.set_ylabel("Density", fontsize=10)
         ax.grid(True, alpha=0.3)
 
 # Add legend to the last subplot
 axes[1, 1].legend(loc='upper right', framealpha=0.9, fontsize=9)
 
-plt.suptitle("Surrogate Test Results: Original (dashed lines) vs Surrogate Distributions", 
-             fontsize=14, fontweight='bold')
+plt.suptitle(
+    "Surrogate Test Results: Original (dashed lines) vs Surrogate Distributions",
+    fontsize=14,
+    fontweight='bold')
 plt.tight_layout()
 plt.savefig('surrogate_results.png', dpi=300, bbox_inches='tight')
 plt.close(fig)
@@ -159,28 +170,28 @@ def compute_surrogate_metrics(signal, method):
 # Section 5: Recurrence Quantification Analysis
 print("\nComputing RQA measures...")
 
+
 def compute_rqa_analysis(signal, regime_name):
     """Compute RQA measures for a given signal."""
     print(f"  Processing {regime_name} regime...")
-    
+
     # Create RQA object with appropriate parameters
     # Use the actual constructor parameters from RQA2.py
     rq = RQA2(data=signal, normalize=True, reqrr=0.05, lmin=2)
-    
+
     # Compute RQA measures
     measures = rq.compute_rqa_measures()
-    
+
     rq.plot_tau_mi_curve(save_path=regime_name + '_tau_mi_plot.png')
-    
+
     rq.plot_fnn_curve(save_path=regime_name + '_fnn_curve_plot.png')
-
-
-
+
     # Get the recurrence plot
     rp = rq.recurrence_plot
-    
+
     return rq, measures, rp
 
+
 # Analyze both regimes
 rqa_results = {}
 for regime, signal in [("Chaotic", x_chaos), ("Periodic", x_per)]:
@@ -191,13 +202,17 @@ def compute_rqa_analysis(signal, regime_name):
             'measures': measures,
             'recurrence_plot': rp
         }
-        
+
         # Print key measures
         print(f"    {regime} RQA measures:")
         for key, value in measures.items():
-            if key in ['recurrence_rate', 'determinism', 'average_diagonal_length', 'max_diagonal_length']:
+            if key in [
+                'recurrence_rate',
+                'determinism',
+                'average_diagonal_length',
+                    'max_diagonal_length']:
                 print(f"      {key}: {value:.4f}")
-                
+
     except Exception as e:
         print(f"    Error computing RQA for {regime}: {e}")
         rqa_results[regime] = None
@@ -206,29 +221,33 @@ def compute_rqa_analysis(signal, regime_name):
 print("\nCreating recurrence plots...")
 fig = plt.figure(figsize=(14, 6))
 
-for i, (regime, signal) in enumerate([("Chaotic", x_chaos), ("Periodic", x_per)]):
-    ax = fig.add_subplot(1, 2, i+1)
-
+for i, (regime, signal) in enumerate(
+        [("Chaotic", x_chaos), ("Periodic", x_per)]):
+    ax = fig.add_subplot(1, 2, i + 1)
+
     if regime in rqa_results and rqa_results[regime] is not None:
         rp = rqa_results[regime]['recurrence_plot']
         measures = rqa_results[regime]['measures']
-        
+
         # Display recurrence plot
         ax.imshow(rp, cmap='binary', origin='lower', aspect='equal')
-        
+
         # Create title with key measures
         title = (f"{regime} Recurrence Plot\n"
-                f"RR={measures['recurrence_rate']:.3f}, "
-                f"DET={measures['determinism']:.3f}\n"
-                f"L={measures['average_diagonal_length']:.2f}, "
-                f"Lmax={measures['max_diagonal_length']:.0f}")
-        
+                 f"RR={measures['recurrence_rate']:.3f}, "
+                 f"DET={measures['determinism']:.3f}\n"
+                 f"L={measures['average_diagonal_length']:.2f}, "
+                 f"Lmax={measures['max_diagonal_length']:.0f}")
+
         ax.set_title(title, fontweight='bold', fontsize=11)
     else:
-        ax.set_title(f"{regime} - Analysis Failed", fontweight='bold', fontsize=11)
-        ax.text(0.5, 0.5, "RQA computation failed", ha='center', va='center', 
-               transform=ax.transAxes, fontsize=12)
-
+        ax.set_title(
+            f"{regime} - Analysis Failed",
+            fontweight='bold',
+            fontsize=11)
+        ax.text(0.5, 0.5, "RQA computation failed", ha='center', va='center',
+                transform=ax.transAxes, fontsize=12)
+
     ax.set_xlabel("Time Index", fontsize=10)
     ax.set_ylabel("Time Index", fontsize=10)
 
@@ -238,9 +257,9 @@ def compute_rqa_analysis(signal, regime_name):
 print("Saved: recurrence_plots.png")
 
 # Section 7: Summary Report
-print("\n" + "="*60)
+print("\n" + "=" * 60)
 print("ANALYSIS SUMMARY")
-print("="*60)
+print("=" * 60)
 
 print(f"\nSystem Parameters:")
 print(f"  Chaotic regime: a={A_CHAOS}, b={B}, c={C}")
@@ -255,18 +274,34 @@ def compute_rqa_analysis(signal, regime_name):
     print(f"\nRQA Results Comparison:")
     print(f"{'Measure':<25} {'Chaotic':<12} {'Periodic':<12}")
     print("-" * 50)
-
-    key_measures = ['recurrence_rate', 'determinism', 'average_diagonal_length', 'max_diagonal_length']
-
+
+    key_measures = [
+        'recurrence_rate',
+        'determinism',
+        'average_diagonal_length',
+        'max_diagonal_length']
+
     for measure in key_measures:
-        chaos_val = rqa_results.get('Chaotic', {}).get('measures', {}).get(measure, np.nan)
-        period_val = rqa_results.get('Periodic', {}).get('measures', {}).get(measure, np.nan)
+        chaos_val = rqa_results.get(
+            'Chaotic',
+            {}).get(
+            'measures',
+            {}).get(
+            measure,
+            np.nan)
+        period_val = rqa_results.get(
+            'Periodic',
+            {}).get(
+            'measures',
+            {}).get(
+            measure,
+            np.nan)
         print(f"{measure:<25} {chaos_val:<12.4f} {period_val:<12.4f}")
 
 print(f"\nFiles generated:")
 print("  - rossler_3d_attractors.png")
-print("  - surrogate_results.png") 
+print("  - surrogate_results.png")
 print("  - recurrence_plots.png")
 
 print(f"\nAnalysis completed successfully!")
-print("="*60)
+print("=" * 60)