Yesterday’s challenge was getting the signal correction pipeline to work. Today’s challenge? Making it production-ready. Time to refactor the preprocessing code into a proper Python package, add comprehensive testing, and set up automated CI/CD for deployment to PyPI.

TLDR: here is the PyPi package

1. Refactoring the signal correction pipeline

The original signal correction pipeline worked great in a Jupyter notebook, but notebook code doesn’t scale well. Here’s what needed to happen:

  1. Extract the logic from notebook cells into a clean, reusable class
  2. Add proper documentation with docstrings for every method
  3. Implement comprehensive unit tests to catch bugs and regressions
  4. Set up CI/CD workflows for automated testing and deployment
  5. Package for PyPI so anyone can install with pip install ariel-data-challenge

2. The SignalCorrection() class

The refactored SignalCorrection() class in ariel_data_preprocessing/signal_correction.py implements the complete 6-step pipeline:

class SignalCorrection:
    '''
    Complete signal correction and calibration pipeline for Ariel telescope data.
    
    This class implements the full 6-step preprocessing pipeline required to transform
    raw Ariel telescope detector outputs into science-ready data suitable for exoplanet
    atmospheric analysis. The pipeline handles both AIRS-CH0 (infrared spectrometer) 
    and FGS1 (guidance camera) data with parallel processing capabilities.
    
    Processing Pipeline:
        1. Analog-to-Digital Conversion (ADC) - Convert raw counts to physical units
        2. Hot/Dead Pixel Masking - Remove problematic detector pixels
        3. Linearity Correction - Account for non-linear detector response
        4. Dark Current Subtraction - Remove thermal background noise
        5. Correlated Double Sampling (CDS) - Reduce read noise via paired exposures
        6. Flat Field Correction - Normalize pixel-to-pixel sensitivity variations
    
    Key Features:
        - Multiprocessing support for parallel planet processing
        - Optional FGS1 downsampling to match AIRS-CH0 cadence
        - Configurable processing steps (can enable/disable individual corrections)
        - Automatic calibration data loading and management
        - HDF5 output for efficient large dataset storage
    
    Performance Optimizations:
        - Process-level parallelization across planets
        - Intelligent FGS downsampling (83% data reduction)
    
    Example:
        >>> corrector = SignalCorrection(
        ...     input_data_path='data/raw',
        ...     output_data_path='data/corrected',
        ...     n_cpus=4,
        ...     downsample_fgs=True,
        ...     n_planets=100
        ... )
        >>> corrector.run()
    
    Input Requirements:
        - Works with Ariel Data Challenge (2025) dataset from Kaggle
        - Raw Ariel telescope data in parquet format
        - Calibration data (dark, dead, flat, linearity correction files)
        - ADC conversion parameters
        - Axis info metadata for timing
    
    Output:
        - HDF5 file with corrected AIRS-CH0 and FGS1 signals and hot/dead pixel masks
        - Organized by planet ID for easy access
        - Reduced data volume (50% reduction from CDS, optional 83% FGS reduction)
        - Science-ready data for downstream analysis
        - Output structure:
        
            HDF5 file structure:
            ├── planet_id_1/
            │   ├── AIRS-CH0_signal       # Corrected spectrometer data
            │   ├── AIRS-CH0_signal_mask  # Mask for spectrometer data
            │   ├── FGS1_signal           # Corrected guidance camera data
            │   └── FGS1_signal_mask      # Mask for guidance camera data
            |
            ├── planet_id_2/
            │   ├── AIRS-CH0_signal       # Corrected spectrometer data
            │   ├── AIRS-CH0_signal_mask  # Mask for spectrometer data
            │   ├── FGS1_signal           # Corrected guidance camera data
            │   └── FGS1_signal_mask      # Mask for guidance camera data
            |
            └── ...
    '''

Each step is now a private method with clear documentation:

  • _ADC_convert() - Applies gain and offset corrections
  • _mask_hot_dead() - Uses sigma clipping to identify hot pixels and masks dead pixels
  • _apply_linear_corr() - Applies polynomial corrections pixel by pixel
  • _clean_dark() - Subtracts scaled dark current
  • _get_cds() - Performs correlated double sampling
  • _correct_flat_field() - Normalizes pixel sensitivity

The class is configurable with ADC parameters, CPU count for parallel processing, and input/output data paths.

3. Comprehensive unit testing

Testing a signal processing pipeline requires careful validation of each step. The test suite in tests/test_preprocessing.py covers:

  • Shape preservation - Ensuring array dimensions are maintained through each step
  • Data type handling - Verifying float64 conversion and masked array creation
  • CDS frame reduction - Confirming the frame count is halved correctly
  • Integration with real data - Using actual calibration files and signal data

Each test uses a subset of real Ariel data to ensure the corrections work with actual telescope outputs, not just synthetic test cases.

4. Automated CI/CD pipeline

Three GitHub workflows handle different aspects of the development pipeline:

4.1. Unit testing (unittest.yml)

Triggered on every pull request to main:

  • Sets up Python 3.8 environment
  • Installs dependencies
  • Runs the complete test suite
  • Prevents merging if any tests fail

4.2. Test PyPI release (test_pypi_release.yml)

Triggered when pushing tags to the dev branch:

  • Builds the package distribution
  • Runs unit tests to ensure quality
  • Publishes to Test PyPI for validation
  • Allows testing the installation process before production release

4.3. Production PyPI release (pypi_release.yml)

Triggered when creating a GitHub release:

  • Builds the final distribution
  • Runs comprehensive tests
  • Publishes to the main PyPI repository
  • Makes the package publicly available via pip install

5. The Benefits

This refactoring effort pays dividends in multiple ways:

5.1. Reproducibility

The Ariel Data Challenge isn’t just about building a working solution - it’s about creating tools that the broader astronomical community can use and improve. Anyone can now install and use the exact same preprocessing pipeline:

pip install ariel-data-preprocessing

5.2. Reliability

Automated testing catches bugs before they reach production. Every code change is validated against real data.

5.3. Maintainability

Clean class structure with documented methods makes the code much easier to understand and modify.

5.4. Collaboration

Other researchers can easily build on this work, contribute improvements, or adapt the pipeline for their own projects.

With the preprocessing pipeline now available as a proper Python package, complete with automated testing and continuous deployment, the foundation is solid for the next phase: building machine learning models to extract exoplanet atmospheric spectra.

6. Next steps

With the infrastructure in place, the focus shifts back to science:

  1. Integrate the package into the main analysis workflow
  2. Optimize performance for batch processing of multiple planets
  3. Build the spectral extraction pipeline using the cleaned data
  4. Develop machine learning models for atmospheric parameter estimation

The engineering detour is complete - time to get back to hunting for exoplanet atmospheres!