Bloodmoon

A Python library for analyzing data from the Wide Field Monitor (WFM) coded mask instrument. Bloodmoon provides tools for:

• Image reconstruction
• Source detection and parameter estimation
• Detector effects modeling (vignetting, PSF)
• Dual-camera source localization

⚠️ Note: Bloodmoon is under active development. APIs may change between versions.

Installation

PyPI

pip install bloodmoon

From Source

Installing from source is necessary when doing development work. The exact process depends on your platform but will generally require:

• Git
• Python 3.11 or later
• pip
• venv or conda (for environment management)

Using venv

# Clone repository
git clone https://github.com/peppedilillo/bloodmoon.git
cd bloodmoon

# Create and activate virtual environment
python3 -m venv venv
source venv/bin/activate  # On Windows: .\venv\Scripts\activate

# Install package in editable mode with development dependencies
pip install -e ".[dev]"

Using Conda

# Clone repository
git clone https://github.com/peppedilillo/bloodmoon.git
cd bloodmoon

# Create and activate conda environment
conda create -n "bloodmoon" python=3.11
conda activate bloodmoon

# Install package in editable mode with development dependencies
pip install -e ".[dev]"

Quick Start

import bloodmoon as bm

# Load camera configuration
wfm = bm.codedmask("wfm_mask.fits")

# Load simulation data
filepaths = bm.simulation_files("simdir/")
sdl1a = bm.simulation(filepaths["cam1a"]["reconstructed"])
sdl1b = bm.simulation(filepaths["cam1b"]["reconstructed"])

# Create detector images
detector, bins = bm.count(wfm, sdl1a.data)

# Reconstruct sky image
sky = bm.decode(wfm, detector)

# Run iterative source detection
for sources, residuals in bm.iros(wfm, sdl1a, sdl1b, max_iterations=10):
    # Process detected sources...
    pass

For more take a look at our demo.

Development

Running Tests

Assuming you installed from sources, and your source living into bloodmon directory.

cd bloodmoon
python -m unittest

Contributing

Contributions are welcome! Before submitting a pull request:

1. Ensure all tests pass: python -m unittest
2. Format code with black: black -l 120 .
3. Sort imports with isort: isort --profile=google .

For bug reports and feature requests, please open an issue.