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…d improve test input size Migrate from pytorch_msssim to torchmetrics for SSIM metrics #17
…res for larger image sizes #17
- Added ChannelAwareBaseModel as an abstract base class for models requiring channel state information (CSI). - Implemented utility methods for CSI validation, normalization, transformation, and feature extraction. - Updated AFModule to inherit from ChannelAwareBaseModel for consistent CSI handling. - Refactored Yilmaz2024DeepJSCCWZ models to extend ChannelAwareBaseModel. - Created a new example script demonstrating the usage of ChannelAwareBaseModel. - Added comprehensive tests for ChannelAwareBaseModel, including validation, normalization, and feature extraction. Create ChannelAwareBaseModel with required CSI support #16
- Implement comprehensive tests for MLPEncoder and MLPDecoder, covering initialization, forward passes, and gradient flow. - Introduce tests for Projection class, including various projection types and their properties. - Validate behavior with different input dimensions, data types, and device compatibility. - Ensure proper handling of trainable and non-trainable projections, along with reproducibility checks for random seeds.
…ark runners and neural compressor - Introduced a new test suite for benchmark runners including StandardRunner, ParallelRunner, ParametricRunner, and ComparisonRunner. - Implemented tests for various scenarios including success, failure, verbose output, and result saving. - Added integration tests to ensure consistency between StandardRunner and ParallelRunner. - Created a comprehensive test suite for NeuralCompressor focusing on real functionality and edge cases. - Included tests for bit-constrained mode, early stopping, and handling of empty batches. - Enhanced coverage by testing various methods and metrics in the NeuralCompressor.
…ark runners and neural compressor - Introduced a new test suite for benchmark runners including StandardRunner, ParallelRunner, ParametricRunner, and ComparisonRunner. - Implemented tests for various scenarios including success, failure, verbose output, and result saving. - Added integration tests to ensure consistency between StandardRunner and ParallelRunner. - Created a comprehensive test suite for NeuralCompressor focusing on real functionality and edge cases. - Included tests for bit-constrained mode, early stopping, and handling of empty batches. - Enhanced coverage by testing various methods and metrics in the NeuralCompressor.
…handling in forward pass
…ira benchmarking system
… of num_bits and improve result filtering in BenchmarkResultsManager
…on for LDPC codes
…ncellation decoding, example of use in communication system with AWGN noise.
… pre-commit errors. - Updated import statements in `__init__.py` for consistency. - Cleaned up the `LDPCCodeEncoder` class by consolidating imports and improving docstring formatting. - Enhanced the `PolarCodeEncoder` class with better documentation and added placeholder methods for decoding and syndrome calculation. - Streamlined the `rptu_database.py` by removing redundant comments and improving citation handling. - Refined utility functions in `utils.py` for clarity and consistency in docstrings. - Minor formatting adjustments in `psk.py` for improved readability.
…ference the correct codeword size
…istency with other PSK modulators
- Implemented comprehensive tests for the polar_code module, including helper functions and the PolarCodeEncoder class. - Added tests for the RPTU database module, ensuring proper structure and functionality of existing codes and database retrieval. - Enhanced utility tests with additional functions such as Taylor_arctanh, sign_to_bin, row_reduction, and others. - Included edge case handling and CUDA compatibility tests for utility functions.
…ions - Introduced a new example script for advanced Polar code visualization, showcasing channel polarization, successive cancellation decoding steps, and performance comparisons between different decoders. - Enhanced the existing Polar simulation example with improved documentation formatting. - Updated the metrics visualization utility to conditionally display legends based on the presence of labels. - Refactored the BeliefPropagationPolarDecoder class by removing unused method documentation. - Simplified the BaseBlockCodeEncoder class by removing inverse_encode and calculate_syndrome method placeholders. - Updated LDPCCodeEncoder and PolarCodeEncoder classes to improve documentation clarity and structure. - Adjusted the API reference generation script to ensure consistent output formatting. - Modified tests for encoder and decoder classes to reflect the removal of deprecated methods and improve error handling.
…ict resolution logic
- Introduced UplinkMACChannel class for modeling multi-user uplink communications. - Implemented channel effects, inter-user interference, and signal combination methods. - Added validation for user channels, gains, and interference power. - Created unit tests for UplinkMACChannel covering initialization, forward pass, error handling, and dynamic updates. - Updated base model methods to accept BaseModel instances for consistency. - Enhanced BPGCompressor documentation and error handling for missing BPG tools. - Added pytest configuration for better test organization and execution.
…ncies #29 feat: enhance testing guidelines and add integration tests for BPGCompressor
- Introduced comprehensive and quick benchmarks for LDPC codes in `ldpc_benchmark.py`, allowing for performance evaluation across multiple configurations and SNR levels. - Implemented a Min-Sum decoder for LDPC codes in `min_sum_ldpc.py`, providing a lower complexity alternative to the belief propagation decoder. - Updated the decoders' registry to include the new Min-Sum decoder and enhanced documentation with relevant references. - Added tests for ECC benchmarks to ensure correctness and reliability of implementations, covering various code families and configurations. - Improved documentation across various modules to include citations and references for algorithms and methods used.
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Pull Request Overview
This PR updates documentation examples, Sphinx configuration, and CI/pre-commit workflows to support the v0.2.0 release of Kaira.
- Refines example gallery indices for constraints, channels, and benchmarks
- Adjusts Sphinx
conf.pyfor new version, gallery dirs, and auto‐examples download - Enhances CI and pre-commit workflows with caching, summaries, and extended matrix
Reviewed Changes
Copilot reviewed 167 out of 167 changed files in this pull request and generated 2 comments.
Show a summary per file
| File | Description |
|---|---|
| docs/examples/constraints/index.rst | Fixed example descriptions and toctree directive |
| docs/examples/channels/index.rst | Updated thumbnails, alt text, tooltips, and toctree |
| docs/conf.py | Bumped version, gallery dirs, env warnings, new hooks |
| .pre-commit-config.yaml | Expanded mypy stubs list |
| .github/workflows/tests.yml | Refined matrix, caching, and test summary steps |
Comments suppressed due to low confidence (5)
docs/examples/constraints/index.rst:65
- The Sphinx directive is missing the double-colon syntax; it should be
.. toctree::to correctly generate the example gallery.
.. toctree:
docs/examples/channels/index.rst:193
- This directive should use
.. toctree::(with::) rather than.. toctree:to ensure the channel examples are included in the build.
.. toctree:
docs/conf.py:409
- This line must be indented under the
try:block to avoid a syntax error; ensure consistent indentation for thetry/exceptclauses.
app.add_config_value("current_date", get_current_date(), "env")
docs/conf.py:325
- Removing the NumPy intersphinx mapping may break cross-references in docs; if there are still references to NumPy types, you should restore this mapping.
- "numpy": ("https://numpy.org/doc/stable", None),
docs/conf.py:29
- Removing the fallback that set
GRAPHVIZ_DOTmay break inheritance diagrams. Consider restoring the environment‐variable assignment or update the warning to reflect the changed behavior.
print("WARNING: graphviz 'dot' command not found in PATH. Continuing without graphviz dot command.")
Codecov ReportAttention: Patch coverage is 📢 Thoughts on this report? Let us know! |
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Signed-off-by: Selim Firat Yilmaz <yilmazselimfirat@gmail.com>
…on codes. The script included encoding, transmission, and decoding processes, along with static and animated visualizations. This change eliminates the file `plot_fec_visual_error_correction.py` from the repository.
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Pull Request: Kaira v0.2.0 Release
Description
This pull request introduces Kaira v0.2.0, a major release that significantly expands the framework's capabilities with comprehensive Forward Error Correction (FEC) implementations, enhanced neural network components, improved channel models, and extensive documentation updates.
Release Date: June 10, 2025
Version: 0.2.0
Previous Version: 0.1.1
Type of Change
🚀 Major New Features
Forward Error Correction (FEC) Framework
Enhanced Communication Systems
Neural Network Components
Metrics and Analysis
🔧 Technical Improvements
Development Infrastructure
Performance Optimizations
🔨 Breaking Changes
bp_iterations→bp_itersin belief propagation decoders📋 Migration Guide
For users upgrading from v0.1.x:
bp_iterationstobp_iterskaira.models.fec.utilsimports🐛 Bug Fixes
Code Quality
FEC Implementation
Testing Framework
📚 Documentation Updates
🧪 Testing
Test Coverage Expansion
Quality Assurance
🔒 Security Improvements
📊 Performance Metrics
Benchmarking Results
🗑️ Removed Components
Legacy Code Cleanup
Development Workflow
📋 Checklist
📝 Additional Information
Key Statistics
Dependencies
Community Impact
This release significantly enhances Kaira's capability as a communication systems research platform, particularly for:
🎯 Next Steps
After this release:
Reviewers: Please pay special attention to:
This release represents a significant milestone in Kaira's development, establishing it as a comprehensive platform for communication systems research and development.