From 7329ddb7487dc32a838fa8bd8cb1b3254d80790c Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 18 Nov 2025 17:02:55 +0000 Subject: [PATCH] Fix YAML workflow syntax and comprehensive documentation updates MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit YAML Fix: - Fixed syntax error on line 79 in .github/workflows/validation.yml - Changed Python script quotes from double to single to avoid YAML parsing issues - Properly escaped inner quotes in f-strings Documentation Consistency Updates: - Updated Python version requirement to 3.11+ across all docs (README, wiki) - Fixed class name inconsistency (GeographicPoint → IonoPoint) in examples - Updated validation pass rate from 83.8% to 86.6% across all documentation - Updated performance metrics to reflect v1.0.1 optimizations (2.3x speedup) - Fixed version references from v1.0.0 to v1.0.1 where applicable - Updated PyPI status to reflect production-ready v1.0.1 Files Modified: - .github/workflows/validation.yml - YAML syntax fix - wiki/Getting-Started.md - Python version, class name, examples - wiki/Known-Issues.md - Performance metrics, version footer - wiki/FAQ.md - Python version, validation rates, performance data - NEXT_STEPS.md - Validation rates, timestamps, status updates All documentation now consistent with v1.0.1 production release status. --- .github/workflows/validation.yml | 34 +++++++++++++------------- NEXT_STEPS.md | 14 +++++------ wiki/FAQ.md | 42 ++++++++++++++++---------------- wiki/Getting-Started.md | 6 ++--- wiki/Known-Issues.md | 20 +++++++-------- 5 files changed, 58 insertions(+), 58 deletions(-) diff --git a/.github/workflows/validation.yml b/.github/workflows/validation.yml index 5037dac..db8099a 100644 --- a/.github/workflows/validation.yml +++ b/.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 diff --git a/NEXT_STEPS.md b/NEXT_STEPS.md index 7be9511..a263b63 100644 --- a/NEXT_STEPS.md +++ b/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 diff --git a/wiki/FAQ.md b/wiki/FAQ.md index 18eb012..b43cbf5 100644 --- a/wiki/FAQ.md +++ b/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) diff --git a/wiki/Getting-Started.md b/wiki/Getting-Started.md index bc5706b..fe0a209 100644 --- a/wiki/Getting-Started.md +++ b/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 diff --git a/wiki/Known-Issues.md b/wiki/Known-Issues.md index e9e79e9..dd216c4 100644 --- a/wiki/Known-Issues.md +++ b/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)