airsplorer 0.5.10

ARIEL AIRS convenience tools for analysis and data manipulation

AIRSplorer

Convenience tools for analysis and data manipulation related to ARIEL AIRS. The package provides utilities for working with images and cubes, basic flux calculations, coordinate conversions, mask handling, I/O helpers, and common plotting routines.

• Python ≥ 3.11
• License: MIT
• Repository: https://github.com/ccossou/airsplorer

Key features

• Image and cube manipulation (load, save, simple transforms)
• Basic photometry and flux/statistics utilities
• Sky/pixel coordinate conversions
• Mask creation, application, and operations
• Plotting helpers and tabulated outputs
• Test suite for core functionality

Indicative modules:

• coord: coordinate conversions and utilities
• flux: simple photometry and flux-related computations
• imager / imlib: image operations
• mask: mask creation and manipulation
• plot: common plotting helpers
• utils: assorted utility functions

Installation

From PyPI (when available):

bash
python -m pip install airsplorer

From source (development):

bash
git clone https://github.com/ccossou/airsplorer.git
cd airsplorer
python -m pip install -e .

Main runtime dependencies are installed automatically.

On windows, one might have to install extra dependencies, namely, Visual C++ Redistributable. Go to Visual Studio, go at the bottom of the page, to the Downloads/Autre outils, infrastructures et redistribuables and take Microsoft Visual C++ v14 Redistributable x64 (or newer if any).

On Mac/Linux, the dependencies should be installed automatically.

[!IMPORTANT] Installation easily fail if it's done in a python environment that is not properly set up, like, for instance, the base environment for spyder/miniforge/miniconda. I advise to use a dedicated environment that is not the system python, or base environment for any virtual environment manager to avoid any potential issues. It is not necessary, however, to create a specific environment for airsplorer and it can be installed in a pre-existing one. Creating a new environment is a good idea if you struggle to make it work, and want to test if it work better in a brand new environment.

Quick start

python
# Example imports (see function docstrings for exact APIs)
from airsplorer import coord, flux, imager, mask, utils, read, write, plot

# Loading and saving (adjust paths and formats to your data)
# data = read.load_image("path/to/file.fits")
# write.save_image(data, "path/to/output.fits")

# Image operations
# img2 = imager.normalize(data)
# m = mask.create_threshold_mask(img2, threshold=5.0)
# img3 = imager.apply_mask(img2, m)

# Flux calculations
# f = flux.aperture_photometry(img3, center=(x0, y0), radius=5)

# Coordinate conversions
# px, py = coord.sky_to_pixel(ra, dec, wcs_header)
# ra2, dec2 = coord.pixel_to_sky(px, py, wcs_header)

# Plotting
# plot.imshow(img3, title="Processed image")

Notes:

• Check each function’s docstring for signatures, units, and options.
• Adjust examples to your data formats and WCS headers.

License and citation

• License: MIT
• If you use airsplorer in scientific work, please cite the repository and the software version you used. A DOI or BibTeX entry may be added in the future.