Takefits

Takefits is a GUI-based astronomical FITS viewer and analysis tool developed by Shunya Takekawa.

Requirements

• Python 3.11 or later

Setup

From PyPI:

pip install takefits

For a local checkout:

python -m venv venv
source venv/bin/activate    # On Windows: venv\Scripts\activate
pip install .

Usage

takefits [path/to/fitsfile]

You can also launch it with:

python -m takefits [path/to/fitsfile]

Takefits can also save your current work using workspace files, so you can resume it anytime. To restore a saved workspace, launch Takefits with the workspace file path.

takefits /path/to/yourfile.workspace.json

Frequency axes open as radio velocity when the file has a rest frequency (RESTFRQ). To see frequencies instead (also for cubes stored in radio velocity, VRAD), change Preferences -> General -> Frequency / velocity axes open as, or choose for one launch:

takefits /path/to/cube.fits --spectral-axis freq   # or --spec freq; "vel" shows velocities

Features

Takefits provides a comprehensive set of tools for radio astronomy data analysis.

1. Multi-View Cube Visualization

Visualize 3D FITS data cubes with synchronized XY, XZ, and ZY planes.

2. Moment Maps & Channel Maps

Calculate moment maps (Integrated Intensity, Velocity Field, Velocity Dispersion) and create tiled channel maps.

3. Interactive P-V Diagram

Interactively draw extraction paths — straight lines, polylines, smooth spline curves, and elliptical arcs — to generate Position-Velocity (P-V) diagrams instantly.

4. Spectrum Analysis

Extract spectra from a single pixel or calculate the average spectrum within selected regions (Circle, Rectangle, Ellipse, Cube).

5. Publication-Quality Figures

Generate publication-quality figures directly from the GUI.

• Contours: Overlay customizable contours with adjustable levels. Contours can also be loaded from another FITS file with a live preview; the dialog shows data range/rms, lets you generate linear Min/Max/Steps levels or edit the Levels field directly, and aligns the resulting contours through the source WCS so images on different grids can be compared without regridding. Double-click an imported overlay in the contour panel to re-edit its levels.
• Markers: Annotate images with symbols, lines, and text.
• Beam Size: Visualize the HPBW ellipse.
• Vector Export: Save plots in PDF, EPS, or SVG formats.

Other Tools

• Regridding: Resample data to a new grid, different coordinate system, or a FITS template.
• Smoothing: Apply Gaussian/Boxcar spatial smoothing and Hanning smoothing along the velocity axis.
• Masking: Apply threshold-based or external masks to data.
• Cutout: Crop data cubes based on regions or coordinate ranges.
• Image Arithmetic: Perform mathematical operations between FITS cubes or with constants using numpy-like expressions.
• Unit Conversion: Convert spectral or intensity units for analysis.
• Baseline Subtraction: Fit and subtract spectral baselines from data cubes.
• Clump Finding: Identify structures using ClumpFind, FellWalker, Dendrogram, and SCIMES (dendrogram clustering) algorithms.

Research use

If you use this software in scientific publications, please cite the Zenodo DOI:

https://doi.org/10.5281/zenodo.18328843

For a specific release, cite the version DOI listed on Zenodo.

Algorithm Citations

The tools below implement established analysis algorithms. If you use them for your analysis, please cite the original papers:

Clump Finding

Cite the paper corresponding to the algorithm you used:

• ClumpFind — Williams, J. P., de Geus, E. J., & Blitz, L. (1994). Determining structure in molecular clouds. The Astrophysical Journal, 428, 693-712. doi:10.1086/174279
• FellWalker — Berry, D. S. (2015). FellWalker-A clump identification algorithm. Astronomy and Computing, 10, 22-31. doi:10.1016/j.ascom.2014.11.004
• Dendrogram — Rosolowsky, E. W., Pineda, J. E., Kauffmann, J., & Goodman, A. A. (2008). Structural Analysis of Molecular Clouds: Dendrograms. The Astrophysical Journal, 679(2), 1338-1351. doi:10.1086/587685
• SCIMES (Dendrogram clustering) — Colombo, D., Rosolowsky, E., Ginsburg, A., Duarte-Cabral, A., & Hughes, A. (2015). Graph-based interpretation of the molecular interstellar medium segmentation. Monthly Notices of the Royal Astronomical Society, 454(2), 2067-2091. doi:10.1093/mnras/stv2063 — a modified copy is bundled under takefits/logic/scimes/ (BSD 3-Clause).

Masking / Moment Analysis

• Rosolowsky, E., & Leroy, A. (2006). Bias-free Measurement of Giant Molecular Cloud Properties. Publications of the Astronomical Society of the Pacific, 118(842), 590-610. doi:10.1086/502982
• Dame, T. M. (2011). The Technique of Velocity-Resolved Moment Masking. arXiv e-prints, arXiv:1101.1499. arXiv:1101.1499

Contact

shunya_at_kanagawa-u.ac.jp