trap - v1.2.1

TRAP v1.2.1

We're pleased to announce the release of TRAP v1.2.1, which introduces several enhancements to improve performance, user experience, and robustness of the reduction pipeline.

🆕 New Features

Signal-based Weighting in Contrast Estimation

• Added use_signal_weighting parameter to the reduction pipeline
• Improves signal-to-noise ratio by weighting pixels based on expected signal strength
• Provides more accurate contrast estimation in challenging observing conditions

Enhanced User Feedback

• New use_progress_bar parameter allows users to enable/disable progress feedback during long-running reductions
• Improves transparency and user experience for time-intensive pipeline operations

🔧 Improvements

Optimized Default Parameters

• Reduced default reduction_mask_size_in_lambda_over_d from 2.0 to 1.0 pixels for better performance in typical science cases
• Expanded default search_region_outer_bound from 55 to 85 pixels to improve detection performance across a wider field of view

Code Quality Enhancements

• Migrated from relative to absolute imports in the regression module for improved maintainability
• Better code organization and cleaner module structure

🐛 Bug Fixes

Robust Candidate Validation

• Improved error handling in template matching and detection pipeline
• Prevents downstream errors when no candidates survive the second iteration
• Enhanced pipeline robustness with clearer user warnings

📦 Installation

bash pip install git+https://github.com/m-samland/trap@v1.2.1

🔗 Full Changelog

For a complete list of changes, see the full changelog.

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Requirements: Python 3.11 or 3.12

Compatibility: This release maintains backward compatibility with existing TRAP workflows while providing new optional features that can be gradually adopted.

- Python
Published by m-samland 7 months ago

trap - v1.2.0 – Parameter Config System, Critical Bug Fixes & Pipeline Stability Improvements

TRAP v1.2.0

TRAP v1.2.0 is a critical stability and maintenance update. This release focuses on improving pipeline robustness, parameter consistency, and ensuring compatibility with recent dependencies.

🚀 Key Highlights

• New Parameter Configuration System: Implemented a parameter configuration system based on Python dataclasses, aligning TRAP closely with the configuration approach used by the spherical pipeline.

⚙️ Improvements & Bug Fixes

• Critical Astropy Compatibility Fix: Corrected FWHM calculation (lambda/D to radian conversion) to restore pipeline accuracy for astropy >= 6.1.
• Robust Ray Server Shutdown: TRAP now safely shuts down existing Ray processes, before opening a new instance.
• Standardized Search Parameters: Ensured consistent defaults for iterative search exclusion radius (15 pixels) and annulus width, now properly propagated through all detection and extraction routines.
• Pickling Consistency: Unified object serialization approach with dill to reliably save and load parameters.
• Minor Syntax and Type Corrections: Eliminated LaTeX warnings in plots and ensured explicit integer handling for boundary parameters.

We strongly recommend all users upgrade to v1.2.0 to ensure continued pipeline reliability. If TRAP supports your research, please cite Samland et al. (2021). Feedback and contributions are always welcome!

- Python
Published by m-samland 8 months ago

trap - v1.1.0

TRAP v1.1.0

TRAP v1.1.0 is a feature-rich update and the first release to include a formal changelog. This release focuses on improving detection capabilities, user experience, and code maintainability. Here are the highlights:

🚀 New Features

• Forced Photometry: Directly extract flux at known positions without relying solely on detection algorithms.
• Save/Load with Pickle: Easily serialize TRAP results or models for later use or sharing.
• Getting Started Tutorial: Includes a Jupyter notebook and data to help users understand TRAP’s full workflow.

🔍 Detection & Performance

• Improved default settings and map processing for better results out-of-the-box.
• Detection maps are now empirically normalized, so values represent detection significance (in σ) for a point source — making results more interpretable and physically meaningful.
• More robust handling of bad/missing data (e.g., zero placeholders or NaNs).
• Template matching with species now functions as expected for spectral data.

🧹 Bug Fixes

• Detection outputs no longer get overwritten during spectral extraction.
• Contrast curves are saved reliably.
• Parameter parsing and masking logic bugs resolved.

📦 Developer & Maintenance

• Modernized packaging using PEP 621 and pyproject.toml.
• Python 3.11+ required (3.9/3.10 dropped).
• Continuous integration workflows and Sphinx docs added.
• Cleaner logging output and internal code cleanup.

We recommend all users upgrade to v1.1.0 to benefit from these improvements. If you use TRAP in your research, please cite Samland et al. (2021). Contributions and feedback welcome!

- Python
Published by m-samland 11 months ago

trap - 1.0.0

- Python
Published by m-samland almost 2 years ago