Fix documentation and GitHub Actions workflow syntax

.github/workflows/validation.yml

@@ -75,32 +75,32 @@ jobs:
           echo "" >> $GITHUB_STEP_SUMMARY
 
           if [ -f validation_reference_results.json ]; then
-            python -c "
+            python -c '
 import json
 import sys
 
-with open('validation_reference_results.json') as f:
+with open("validation_reference_results.json") as f:
     results = json.load(f)
 
-summary = results['summary']
-targets = results['targets']
+summary = results["summary"]
+targets = results["targets"]
 
-print(f\"**Test Cases Run:** {summary['test_cases_run']}\")
-print(f\"**Total Comparisons:** {summary['total']}\")
-print(f\"**Passed:** {summary['passed']} ({summary['pass_rate']:.1f}%)\")
-print(f\"**Failed:** {summary['failed']}\")
+print(f"**Test Cases Run:** {summary[\"test_cases_run\"]}")
+print(f"**Total Comparisons:** {summary[\"total\"]}")
+print(f"**Passed:** {summary[\"passed\"]} ({summary[\"pass_rate\"]:.1f}%)")
+print(f"**Failed:** {summary[\"failed\"]}")
 print()
 
-pass_rate = summary['pass_rate']
-if pass_rate >= targets['excellent_pass_rate']:
-    print(f\"✅ **EXCELLENT** - Pass rate {pass_rate:.1f}% exceeds target {targets['excellent_pass_rate']}%\")
-elif pass_rate >= targets['target_pass_rate']:
-    print(f\"✅ **VERY GOOD** - Pass rate {pass_rate:.1f}% exceeds target {targets['target_pass_rate']}%\")
-elif pass_rate >= targets['minimum_pass_rate']:
-    print(f\"✅ **PASSED** - Pass rate {pass_rate:.1f}% meets minimum {targets['minimum_pass_rate']}%\")
+pass_rate = summary["pass_rate"]
+if pass_rate >= targets["excellent_pass_rate"]:
+    print(f"✅ **EXCELLENT** - Pass rate {pass_rate:.1f}% exceeds target {targets[\"excellent_pass_rate\"]}%")
+elif pass_rate >= targets["target_pass_rate"]:
+    print(f"✅ **VERY GOOD** - Pass rate {pass_rate:.1f}% exceeds target {targets[\"target_pass_rate\"]}%")
+elif pass_rate >= targets["minimum_pass_rate"]:
+    print(f"✅ **PASSED** - Pass rate {pass_rate:.1f}% meets minimum {targets[\"minimum_pass_rate\"]}%")
 else:
-    print(f\"❌ **BELOW TARGET** - Pass rate {pass_rate:.1f}% below minimum {targets['minimum_pass_rate']}%\")
-" >> $GITHUB_STEP_SUMMARY
+    print(f"❌ **BELOW TARGET** - Pass rate {pass_rate:.1f}% below minimum {targets[\"minimum_pass_rate\"]}%")
+' >> $GITHUB_STEP_SUMMARY
           else
             echo "❌ Validation failed to produce results" >> $GITHUB_STEP_SUMMARY
           fi

NEXT_STEPS.md

@@ -59,7 +59,7 @@ DVOACAP-Python v1.0.1 is production-ready with **86.6% validation pass rate** ac
 
 ## ~~Priority 1: Fix Phase 5 Integration (Weeks 1-2)~~ ✅ **COMPLETED**
 
-**Status:** Phase 5 validation achieved **83.8% pass rate** (181/216 tests), exceeding the 80% target.
+**Status:** Phase 5 validation achieved **86.6% pass rate** (226/261 tests), exceeding the 85% target.
 
 **Completed Work:**
 - ✅ Reliability calculation verified against FORTRAN RELBIL.FOR
@@ -69,7 +69,7 @@ DVOACAP-Python v1.0.1 is production-ready with **86.6% validation pass rate** ac
 - ✅ All core algorithms match FORTRAN reference
 
 **Results:**
-- Tangier → Belgrade test path: 83.8% pass rate (181/216 comparisons)
+- Total validation: 86.6% pass rate (226/261 comparisons across 11 test cases)
 - Predictions show valid reliability percentages (0-100%)
 - No crashes or exceptions on valid inputs
 - Signal strength predictions in expected range
@@ -85,7 +85,7 @@ DVOACAP-Python v1.0.1 is production-ready with **86.6% validation pass rate** ac
 **Completed Work:**
 - ✅ Created reference validation test suite (`test_voacap_reference.py`)
 - ✅ Established tolerance specifications (SNR ±10 dB, Reliability ±20%, MUF ±2 MHz)
-- ✅ Achieved 83.8% pass rate on baseline test path
+- ✅ Achieved 86.6% pass rate across 11 diverse test paths
 - ✅ CI/CD workflow implemented (`.github/workflows/validation.yml`)
 - ✅ Validation status badge added to README
 
@@ -493,7 +493,7 @@ DVOACAP-Python v1.0.1 is production-ready with **86.6% validation pass rate** ac
 ### ~~Weeks 1-2: Critical Bug Fixes~~ ✅ **COMPLETED**
 - ✅ Fixed Phase 5 reliability calculation
 - ✅ Validated signal strength computations
-- ✅ Basic predictions working correctly (83.8% pass rate)
+- ✅ Basic predictions working correctly (86.6% pass rate)
 - **Milestone:** Predictions show >0% reliability, one path validates ✓
 
 ### ~~Weeks 3-4: Systematic Validation~~ ✅ **COMPLETED**
@@ -625,7 +625,7 @@ python validate_predictions.py
 
 ---
 
-**Last Updated:** 2025-11-15
-**Status:** Phase 5 complete (83.8% validation). Focus: Expand test coverage, documentation maintenance
+**Last Updated:** 2025-11-18
+**Status:** Phase 5 complete (86.6% validation, exceeds 85% target). v1.0.1 released with 2.3x performance boost. Focus: Documentation polish, PyPI preparation
 
-**Next Review:** After expanding test coverage to 7+ diverse paths
+**Next Review:** Before PyPI public release

wiki/FAQ.md

@@ -55,11 +55,11 @@ DVOACAP-Python is a Python port of the DVOACAP HF propagation prediction engine,
 
 ### How accurate is DVOACAP-Python?
 
-**Current validation status:**
+**Current validation status (v1.0.1):**
 - **Phase 1-4 (Geometry, Solar, Ionosphere, Raytracing):** >95% validation pass rate
-- **Phase 5 (Signal Predictions):** 83.8% validation pass rate
+- **Phase 5 (Signal Predictions):** 86.6% validation pass rate
 
-This means 83.8% of predictions match reference VOACAP output within acceptable tolerances. The remaining 16.2% show discrepancies being investigated and resolved.
+This means 86.6% of predictions match reference VOACAP output within acceptable tolerances. The remaining 13.4% show minor discrepancies at edge cases.
 
 See [Validation Status](Validation-Status) and [Known Issues](Known-Issues) for details.
 
@@ -106,12 +106,12 @@ Yes! DVOACAP-Python is open source under the **MIT License**. You can use it fre
 ### What are the system requirements?
 
 **Minimum:**
-- Python 3.8 or higher
+- Python 3.11 or higher
 - 200 MB disk space
 - 512 MB RAM
 
 **Recommended:**
-- Python 3.10 or higher
+- Python 3.11 or higher
 - 500 MB disk space (with dependencies)
 - 1 GB RAM
 
@@ -155,15 +155,15 @@ The only data files needed are the CCIR/URSI coefficient maps, which are include
 
 ### Can I install DVOACAP-Python with pip from PyPI?
 
-**Not yet.** DVOACAP-Python is currently in active development and not published to PyPI. Install from source using:
+**Not yet.** DVOACAP-Python v1.0.1 is production-ready but not yet published to PyPI. Install from source using:
 
 ```bash
 pip install -e .
 ```
 
-Once the project reaches v1.0, it will be published to PyPI for easier installation with:
+PyPI publication is planned for a future release:
 ```bash
-pip install dvoacap  # Future release
+pip install dvoacap  # Planned future release
 ```
 
 ---
@@ -277,16 +277,16 @@ print(f"Long path: {long_distance:.0f} km")
 
 ## Accuracy & Validation
 
-### Why is the validation pass rate only 83.8%?
+### Why is the validation pass rate 86.6%?
 
-The 83.8% pass rate for Phase 5 indicates that **83.8% of predictions match reference VOACAP within tolerances**. The remaining 16.2% show discrepancies due to:
+The 86.6% pass rate for Phase 5 indicates that **86.6% of predictions match reference VOACAP within tolerances** across 11 diverse test cases. The remaining 13.4% show minor discrepancies due to:
 
 1. **Numerical differences** - Python vs. Pascal floating-point operations
 2. **Mode selection variations** - Different propagation mode choices
 3. **Edge cases** - Extreme solar conditions, high latitudes, very short/long paths
 4. **Over-the-MUF handling** - Different approaches when frequency > MUF
 
-**Important:** Even "failed" test cases typically show SNR differences < 3 dB, which is operationally acceptable for amateur radio.
+**Important:** This validation rate exceeds the 85% target threshold and is considered production-ready. Even "failed" test cases typically show acceptable operational differences.
 
 See [Known Issues](Known-Issues) for details.
 
@@ -306,22 +306,22 @@ The dashboard includes optional PSKreporter validation.
 
 ## Performance
 
-### Why are predictions slow?
+### How fast are predictions in v1.0.1?
 
-DVOACAP predictions are **computationally intensive** because they perform full raytracing through a 3D ionospheric model.
+DVOACAP v1.0.1 delivers a **2.3x performance boost** over v1.0.0 through algorithmic optimizations. While predictions remain computationally intensive (full raytracing through 3D ionospheric model), they are now much faster.
 
-**Typical times:**
-- Single prediction: ~200-500 ms
-- Full band sweep (7 frequencies): ~2-3 seconds
-- 24-hour forecast (12 time points): ~25-35 seconds
-- Full dashboard (10 regions × 7 bands): ~60-90 seconds
+**Typical times (v1.0.1):**
+- Single prediction: ~4 ms (was ~8 ms in v1.0.0)
+- Multi-frequency (9 frequencies): ~48 ms (was ~111 ms, 2.3x faster)
+- 24-hour scan: ~118 ms (was ~282 ms, 2.4x faster)
+- Area coverage (100 predictions): ~350 ms (was ~820 ms, 2.3x faster)
 
-**Why it's slow:**
+**Computational bottlenecks:**
 - Iterative raytracing (Phase 4)
 - CCIR coefficient processing (Phase 3)
 - Python overhead vs. compiled code
 
-See [Performance Tips](Performance-Tips) for optimization strategies.
+See [Performance Tips](Performance-Tips) for optimization strategies and benchmarks.
 
 ---
 
@@ -442,7 +442,7 @@ See [Contributing Guide](Contributing) for guidelines.
 ### What development tools do I need?
 
 **Essential:**
-- Python 3.8+
+- Python 3.11+
 - pytest (testing)
 - black (formatting)
 - flake8 (linting)

wiki/Getting-Started.md

@@ -7,7 +7,7 @@ This guide will help you install DVOACAP-Python and run your first propagation p
 
 ## Prerequisites
 
-- Python 3.8 or higher
+- Python 3.11 or higher
 - pip (Python package manager)
 - git (for cloning the repository)
 
@@ -96,7 +96,7 @@ pytest tests/test_path_geometry.py -v
 This example computes ionospheric parameters at a single location:
 
 ```python
-from dvoacap import FourierMaps, ControlPoint, GeographicPoint, compute_iono_params
+from dvoacap import FourierMaps, ControlPoint, IonoPoint, compute_iono_params
 import math
 
 # Load CCIR/URSI ionospheric maps
@@ -105,7 +105,7 @@ maps.set_conditions(month=6, ssn=100, utc_fraction=0.5)  # June, SSN=100, noon U
 
 # Create control point at Philadelphia
 pnt = ControlPoint(
-    location=GeographicPoint.from_degrees(40.0, -75.0),
+    location=IonoPoint.from_degrees(40.0, -75.0),
     east_lon=-75.0 * math.pi/180,
     distance_rad=0.0,
     local_time=0.5,  # Noon local

wiki/Known-Issues.md

@@ -64,20 +64,20 @@ Focus on SNR and reliability values rather than mode names.
 
 ## Performance Limitations
 
-### Slow Prediction Generation
+### Prediction Performance (Optimized in v1.0.1)
 
-**Status:** Known Limitation
-**Impact:** Medium
+**Status:** Significantly Improved
+**Impact:** Low (after v1.0.1 optimization)
 **Affects:** Dashboard generation, batch processing
 
 **Description:**
-Full VOACAP predictions are computationally intensive. Generating predictions for 10 regions × 7 bands × 12 time points can take 60-90 seconds.
+Version 1.0.1 introduced comprehensive algorithmic optimizations providing a 2.3x speedup. Full VOACAP predictions remain computationally intensive but are now much faster.
 
-**Timing Breakdown:**
-- Single frequency prediction: ~200-500ms
-- Full band sweep (7 frequencies): ~2-3 seconds
-- 24-hour forecast (12 time points): ~25-35 seconds
-- Full dashboard (10 regions × 7 bands × 12 hours): ~60-90 seconds
+**Timing Breakdown (v1.0.1):**
+- Single frequency prediction: ~4ms (was ~8ms in v1.0.0)
+- Full band sweep (9 frequencies): ~48ms (was ~111ms, 2.3x faster)
+- 24-hour forecast (12 time points): ~118ms (was ~282ms, 2.4x faster)
+- Area coverage (100 predictions): ~350ms (was ~820ms, 2.3x faster)
 
 **Root Causes:**
 1. Raytracing calculations (Phase 4) - iterative path finding
@@ -380,4 +380,4 @@ If you encounter issues not listed here:
 ---
 
 **Last Updated:** 2025-11-18
-**Project Status:** v1.0.0 Production Ready (86.6% validation accuracy)
+**Project Status:** v1.0.1 Production Ready (86.6% validation accuracy, 2.3x performance boost)