minor improvements for analysis and reporting code

Author: networmix

docs/reference/api-full.md

@@ -10,7 +10,7 @@ For a curated, example-driven API guide, see **[api.md](api.md)**.
 > - **[CLI Reference](cli.md)** - Command-line interface
 > - **[DSL Reference](dsl.md)** - YAML syntax guide
 
-**Generated from source code on:** July 26, 2025 at 15:00 UTC
+**Generated from source code on:** July 27, 2025 at 07:33 UTC
 
 **Modules auto-discovered:** 51
 

ngraph/workflow/analysis/capacity_matrix.py

@@ -9,6 +9,7 @@
 import importlib
 from typing import Any, Dict, List, Optional
 
+import matplotlib.pyplot as plt
 import pandas as pd
 
 from .base import NotebookAnalyzer
@@ -412,9 +413,13 @@ def analyze_and_display_flow_availability(
             f"   Median Flow:  {stats['median_flow']:.2f} ({stats['flow_percentiles']['p50']['relative']:.1f}%)"
         )
         print(
-            f"   Std Dev:      {stats['flow_std']:.2f} ({stats['relative_std']:.1f}%)"
+            f"   Std Dev:      {stats['flow_std']:.2f} ({stats['relative_std']:.1f}%) "
+            f"→ Flow dispersion magnitude relative to mean"
+        )
+        print(
+            f"   CV:           {stats['coefficient_of_variation']:.1f}% "
+            f"→ Normalized variability metric: <30% stable, >50% high variance\n"
         )
-        print(f"   CV:           {stats['coefficient_of_variation']:.1f}%\n")
 
         print("📈 Flow Distribution Percentiles:")
         for p_name in ["p5", "p10", "p25", "p50", "p75", "p90", "p95", "p99"]:
@@ -427,22 +432,33 @@ def analyze_and_display_flow_availability(
         print()
 
         print("🎯 Network Reliability Analysis:")
-        for reliability in ["99%", "95%", "90%", "80%"]:
+        for reliability in ["99.99%", "99.9%", "99%", "95%", "90%", "80%"]:
             flow_fraction = viz_data["reliability_thresholds"].get(reliability, 0)
             flow_pct = flow_fraction * 100
             print(f"   {reliability} reliability: ≥{flow_pct:5.1f}% of maximum flow")
         print()
 
         print("📐 Distribution Characteristics:")
         dist_metrics = viz_data["distribution_metrics"]
-        print(f"   Gini Coefficient:     {dist_metrics['gini_coefficient']:.3f}")
-        print(f"   Quartile Coefficient: {dist_metrics['quartile_coefficient']:.3f}")
-        print(f"   Range Ratio:          {dist_metrics['flow_range_ratio']:.3f}\n")
+        gini = dist_metrics["gini_coefficient"]
+        quartile = dist_metrics["quartile_coefficient"]
+        range_ratio = dist_metrics["flow_range_ratio"]
 
-        # Try to render plots (optional)
-        try:
-            import matplotlib.pyplot as plt
+        print(
+            f"   Gini Coefficient:     {gini:.3f} "
+            f"→ Flow inequality: 0=uniform, 1=maximum inequality"
+        )
+        print(
+            f"   Quartile Coefficient: {quartile:.3f} "
+            f"→ Interquartile spread: (Q3-Q1)/(Q3+Q1), measures distribution skew"
+        )
+        print(
+            f"   Range Ratio:          {range_ratio:.3f} "
+            f"→ Total variation span: (max-min)/max, failure impact magnitude\n"
+        )
 
+        # Render plots for flow availability analysis
+        try:
             cdf_data = viz_data["cdf_data"]
             percentile_data = viz_data["percentile_data"]
             fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))
@@ -476,8 +492,6 @@ def analyze_and_display_flow_availability(
 
             plt.tight_layout()
             plt.show()
-        except ImportError:
-            print("Matplotlib not available for visualisation")
         except Exception as exc:  # pragma: no cover
             print(f"⚠️  Visualisation error: {exc}")
 
@@ -689,7 +703,7 @@ def _prepare_flow_cdf_visualization_data(
             percentiles.append(avail_prob)
             flow_at_percentiles.append(rel_flow)
 
-        reliability_thresholds = [99, 95, 90, 80, 70, 50]
+        reliability_thresholds = [99.99, 99.9, 99, 95, 90, 80, 70, 50]
         threshold_flows: Dict[str, float] = {}
         for threshold in reliability_thresholds:
             target_avail = threshold / 100

ngraph/workflow/analysis/flow_analyzer.py

@@ -8,6 +8,7 @@
 import importlib
 from typing import Any, Dict
 
+import matplotlib.pyplot as plt
 import pandas as pd
 
 from .base import NotebookAnalyzer
@@ -122,8 +123,6 @@ def display_analysis(self, analysis: Dict[str, Any], **kwargs) -> None:
         viz_data = analysis["visualization_data"]
         if viz_data["has_multiple_steps"]:
             try:
-                import matplotlib.pyplot as plt
-
                 fig, ax = plt.subplots(figsize=(12, 6))
 
                 for step in viz_data["steps"]:
@@ -140,8 +139,8 @@ def display_analysis(self, analysis: Dict[str, Any], **kwargs) -> None:
                 ax.legend()
                 plt.tight_layout()
                 plt.show()
-            except ImportError:
-                print("Matplotlib not available for visualization")
+            except Exception as exc:  # pragma: no cover
+                print(f"⚠️  Visualization error: {exc}")
 
     def analyze_capacity_probe(self, results: Dict[str, Any], **kwargs) -> None:
         """Analyze and display capacity probe results for a specific step.

ngraph/workflow/analysis/package_manager.py

@@ -69,6 +69,7 @@ def setup_environment(cls) -> Dict[str, Any]:
             itables_opt.lengthMenu = [10, 25, 50, 100, 500, -1]
             itables_opt.maxBytes = 10**7  # 10MB limit
             itables_opt.maxColumns = 200  # Allow more columns
+            itables_opt.showIndex = True  # Always show DataFrame index as a column
 
             # Configure warnings
             import warnings

tests/integration/test_scenario_4.py

@@ -7,7 +7,7 @@
 - Bracket expansion in group names for multiple pattern matching
 - Complex node and link override patterns with advanced regex
 - Risk groups with hierarchical structure and failure simulation
-- Advanced workflow steps (EnableNodes, DistributeExternalConnectivity, NotebookExport)
+- Advanced workflow steps (EnableNodes, DistributeExternalConnectivity)
 - NetworkExplorer integration for hierarchy analysis
 - Large-scale network topology with realistic data center structure