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WaveDiff / WF-PSF Changelog

All notable changes to WaveDiff are documented in this file.

[Unreleased]

  • Changes in progress for the next release.

v3.1.0 — 2026-02-23

New features

  • Added PSF inference capabilities for generating broadband (polychromatic) PSFs from trained models given star positions and SEDs
  • Added run_type attribute to DataHandler supporting training, simulation, metrics, and inference modes
  • Implemented ZernikeInputsFactory class for building ZernikeInputs instances based on run type
  • Added psf_model_loader.py module for centralized model weights loading
  • Added configurable optimizer selection system via new optimizer.py module with get_optimizer function
  • Added support for hyperparameter overrides (learning rate, beta1/beta2, epsilon, amsgrad) via YAML or programmatic configuration
  • RectifiedAdam optimizer now dynamically imports TensorFlow Addons only when explicitly specified in configuration

Bug fixes

  • Fix logger formatting for relative RMSE metrics in metrics.py (values were not being displayed)

Internal changes

  • Added pre-commit hooks for code quality, formatting, and changelog enforcement
  • Refactored TFPhysicalPolychromatic and related modules to separate training vs. inference behavior
  • Enhanced ZernikeInputs data class with intelligent assembly based on run type and available data
  • Implemented hybrid loading pattern with eager loading in constructors and lazy-loading via property decorators
  • Centralized PSF data extraction in data_handler module
  • Improved code organization with new tf_utils.py module in psf_models sub-package
  • Updated configuration handling to support inference workflows via inference_config.yaml
  • Fixed incorrect argument name in DataHandler that prevented proper TensorFlow data type conversion
  • Removed deprecated get_obs_positions method
  • Updated documentation to include inference package
  • Refactored build_PSF_model to accept either Keras optimizer instances or configuration passed through get_optimizer
  • Added interpolation.py and types.py modules with vendored code from TensorFlow Addons repository
  • Replaced tfa.image.interpolate_spline with local tfa_interpolate_spline_rbf implementation
  • Added comprehensive unit tests in test_optimizer.py and test_interpolation.py
  • Updated README and added THIRD_PARTY_LICENSE directory with TensorFlow Addons license
  • Training now runs on TensorFlow 2.11 without requiring TensorFlow Addons installation
  • Removed TensorFlow Addons as a required dependency; RectifiedAdam optimizer now requires explicit TFA installation if needed
  • Remove deprecated/optional import tensorflow-addons statement from tf_layers.py
  • Fixed Sphinx autosummary import errors by removing core dependencies (tensorflow) from autodoc_mock_imports in conf.py.
  • Updated pyproject.toml to include all wf_psf packages under src/ when executing CD
  • Updated example configuration files with clearer inline comments.
  • Generated API documentation for new inference package in api.rst
  • Generated API documentation for new instrument package in api.rst
  • Inference Configuration section in configuration.md documenting inference_config.yaml
  • Restructured Configuration documentation:
    • Split workflows into "CLI Tasks" and "Additional Components" sections
    • Added configuration file dependency table showing required vs optional files per task
    • Clarified configuration filename flexibility (filenames customizable, internal structure fixed)
    • Standardized section titles (Training Configuration, Metrics Configuration, etc.)
    • Improved markdown formatting and fixed broken anchor links
  • Updated dependencies.md to document tensorflow-addons as optional dependency with manual installation instructions
  • tensorflow-addons from core dependencies documentation (now documented as optional)

[3.0.0] – 2026-01-20

Major update with PSF model refactoring, masked training, and CI/doc improvements.

Breaking changes

  • Removed the --repodir argument in CLI; scripts using it will now fail.

New features

  • Added physical layer model with improved modularity (TFPhysicalPolychromatic refactor) and configurable parameters.
  • Introduced rotation of obscuration mask with configurable parameter.
  • Updated CentroidEstimator to support mask-based estimation.
  • Added options for user-configurable flags:
    • use_prior for Zernike prior
    • correct_centroids and sigma_centroid_window for centroid error correction
    • add_ccd_misalignments and ccd_misalignments_input_path for CCD misalignment correction
  • Added option to randomize the data-driven part seed for reproducibility.
  • Added phase retrieval projection algorithm considering obscurations.
  • Masked training and evaluation: added masked_mse loss and MaskedMeanSquaredErrorMetric classes.
  • Added configurable parameter for computing shape metrics for test datasets as optional.
  • Added new sigmoid parameters to apply to the sample weights.
  • Added masked datasets and corresponding generation notebooks.

Bug fixes

  • Fixed missing e₂ and R₂ shape metric plots
  • Fixed broken contribution link in documentation
  • Corrected bug in MonochromaticMetricsPlotHandler class regarding eval_mono_metric configuration.

Performance improvements

  • Improved numerical stability and reproducibility in training routines

Internal changes

  • Replaced Black with Ruff for linting and formatting.
  • Updated TensorFlow to 2.11 (compatibility fixes for NumPy ≥ 1.26.4 and Astropy).
  • Reorganized modules for clarity (e.g., SimPSFToolkit.py renamed).
  • Improved PEP8 compliance across the codebase.
  • Introduced Scriv-based changelog infrastructure.
  • Configured sphinx.ext.autosummary to auto-generate stubs in _autosummary/.
  • Added new documentation and templates: CODE_OF_CONDUCT.md, CONTRIBUTING.md, DEV_WORKFLOW.md.