spxquery 0.2.1

SPHEREx time-domain analysis package for querying and analyzing spectral image data

SPXQuery - SPHEREx Time-Domain Analysis Package

SPXQuery is a Python package designed to automate SPHEREx spectral image data query, download, and time-domain analysis for astronomical sources.

Example Output

Automated spectral and temporal analysis of the Cloverleaf quasar (z=2.56) showing SPHEREx's multi-wavelength time-domain capabilities.

Features

• Automated data pipeline: Query → Download → Photometry → Visualization
• Image cutout support: Download only regions of interest (99% storage reduction)
• Parallel processing: Fast downloads and photometry extraction
• Resumable execution: Automatic state persistence for interrupted runs
• Quality control: Built-in filtering with visual inspection of rejected data
• Publication-ready plots: Combined spectral and time-series visualization

Installation

Requirements

• Python 3.11 or later (supports Python 3.11-3.13)

Install from PyPI (Recommended)

pip install spxquery

Verify installation:

python -c "import spxquery; print(spxquery.__version__)"

Installation from Source

For the latest development version:

git clone https://github.com/WenkeRen/spxquery.git
cd spxquery
pip install .

For Developers (Optional)

If you want to contribute to SPXQuery or manage dependencies with Poetry:

# Install Poetry
curl -sSL https://install.python-poetry.org | python3 -

# Install with Poetry
poetry install

For detailed installation instructions and troubleshooting, see INSTALL.md.

Quick Start

One-Line Pipeline

from spxquery.core.pipeline import run_pipeline

# Run complete pipeline for Cloverleaf quasar (z=2.56)
run_pipeline(
    ra=213.9427080,
    dec=11.4953890,
    output_dir="output",
    source_name="cloverleaf",
    aperture_diameter=2.0,
    cutout_size="60px"
)

Basic Usage

Configure and Run

from spxquery import Source, QueryConfig, SPXQueryPipeline

# Configure your analysis for Cloverleaf quasar
source = Source(ra=213.9427080, dec=11.4953890, name="cloverleaf")
config = QueryConfig(
    source=source,
    output_dir="output",
    aperture_diameter=2.0,
    cutout_size="60px"
)

# Run the pipeline
pipeline = SPXQueryPipeline(config)
pipeline.run_full_pipeline()

Step-by-Step Execution

pipeline.run_query()          # Query IRSA archive
pipeline.run_download()       # Download FITS files
pipeline.run_processing()     # Extract photometry
pipeline.run_visualization()  # Create plots

Resume After Interruption

# Automatically resume from saved state
pipeline = SPXQueryPipeline(config)
pipeline.resume()

For detailed examples including quality control settings, band selection, and advanced features, see example/quickstart_demo.ipynb.

Output Files

The pipeline creates an organized output directory:

output_dir/
├── data/                    # Downloaded FITS files (by band)
├── results/
│   ├── lightcurve.csv       # Photometry time-series (all measurements)
│   ├── combined_plot.png    # Spectral + temporal visualization
│   ├── query_summary.json   # Query metadata
└── {source_name}.json       # Pipeline state (for resume)

The lightcurve.csv contains all photometry measurements with columns including MJD, flux, wavelength, band, quality flags, and SNR.

SPHEREx Bands

Band	Wavelength Range	Resolving Power
D1	0.75-1.09 μm	R=39
D2	1.10-1.62 μm	R=41
D3	1.63-2.41 μm	R=41
D4	2.42-3.82 μm	R=35
D5	3.83-4.41 μm	R=112
D6	4.42-5.00 μm	R=128

Documentation

• Installation Guide: Detailed installation instructions and troubleshooting
• Quick Start Notebook: Interactive tutorial with examples
• API Reference: Source code with docstrings

Key Features Explained

Image Cutouts: Download 200×200 pixel cutouts (~0.7 MB) instead of full 2040×2040 images (~70 MB) for 99% storage reduction. Specify cutout_size="200px" or use angular units like "3arcmin".

Quality Control: All measurements are saved to CSV. Visualization applies configurable filters (sigma_threshold, bad_flags) to show good measurements (filled circles) and rejected data (gray crosses) for inspection.

Resume Capability: Pipeline automatically saves state after each stage. Use pipeline.resume() to continue from interruptions without re-downloading data.

Known Issues

Overly Conservative Source Masking: The pipeline uses official SPHEREx image masks to avoid bad pixels during background subtraction. However, the official pipeline tends to provide overly large source masks. If your target is located in an extended nebula or near bright stars, the photometry extraction may fail because no clean pixels are available for background estimation. In such cases, consider using a different background estimation method or manually adjusting the mask parameters.

License

This package is provided as-is for SPHEREx data analysis.

Acknowledgments

Based on SPHEREx data from NASA/IPAC Infrared Science Archive (IRSA).