GalCubeCraft 0.1.2

A synthetic IFU spectral cube generator with a theoretical rotating galaxy disk model

     

High-fidelity simulator for synthetic IFU (Integral Field Unit) spectral cubes.

GalCubeCraft provides a compact, well-documented pipeline to build 3D spectral cubes that mimic observations of disk galaxies. It combines simple analytic galaxy models (Sérsic light profiles + exponential vertical structure), simple rotation-curve kinematics, viewing-angle projections and instrument effects (beam convolution, channel binning) to produce a physically motivated basis and test data for algorithm development, denoising, and visualization.

This README explains the science and mathematics behind the generator, how to install the package, and several practical examples for quick experimentation.

Table of contents

• What GalCubeCraft does
• Scientific background & equations
• Installation (PyPI + source)
• Quick start examples
• API reference (minimal)
• Reproducibility, limitations, and troubleshooting
• Credits & citation

What GalCubeCraft does

GalCubeCraft synthesizes spectral datacubes with dimensions (n_velocity, ny, nx). Each cube contains one or more galaxy components. For each galaxy component the generator:

• Builds a 3D flux density field using a Sérsic profile in the disk plane combined with an exponential vertical profile.
• Computes an analytic circular velocity field from a compact rotation-curve model and assigns tangential velocities to voxels.
• Rotates the 3D flux and velocity fields to a chosen viewing geometry.
• Projects emission into line-of-sight velocity bins to produce a spectral cube.
• Optionally convolves each 2D channel with a telescope beam and saves cubes to data/raw_data/<nz>x<ny>x<nx>/cube_*.npy.

The package is intentionally clear and inspectable (readable loops, compact functions), making it suitable for method development and teaching.

Scientific background & equations

This section summarises the main mathematical building blocks implemented in the code: the Sérsic flux distribution, vertical exponential profile, and the analytical rotation curve used to assign tangential velocities.

Sérsic radial profile (disk plane)

The radial surface brightness (Sérsic) profile is given by

$$S_r(r) = S_e \exp\left[-b_n\left(\left(\frac{r}{R_e}\right)^{1/n} - 1\right)\right]$$

where

• $S_e$ is the flux density at the effective radius $R_e$,
• $n$ is the Sérsic index that controls the concentration,
• $b_n$ is a constant that depends on $n$ (approximated by a series expansion).

The package uses the standard series expansion for $b_n$:

$$b_n(n) \approx 2n - \tfrac{1}{3} + \frac{4}{405n} + \frac{46}{25515n^2} + \cdots$$

Vertical exponential profile

Galaxies are modeled with an exponential vertical fall-off: $$S_z(z) = \exp\left(-\frac{|z|}{h_z}\right)$$

Combining radial and vertical profiles gives the 3D flux density used in the generator:

$$S(x,y,z) = S_e ; \exp\left[-b_n\left(\left(\frac{r}{R_e}\right)^{1/n} - 1\right)\right]; \exp\left(-\frac{|z|}{h_z}\right)$$

with $r = \sqrt{x^2 + y^2}$ in the disk plane.

Analytical rotation curve

To assign tangential velocities the implementation uses a compact empirical approximation (implemented as milky_way_rot_curve_analytical):

$$v(R) = v_0 \times 1.022 \times \left(\frac{R}{R_0}\right)^{0.0803}$$

where $v_0$ is a characteristic velocity scale and $R_0$ is derived from the effective radius and Sérsic index (see code comments for details). This simple form reproduces the gently rising/flat behaviour of typical disk-galaxy rotation curves at the scales of interest for IFU-like synthetic data.

Beam convolution and FWHM to σ relation

When simulating instrument resolution we convolve 2D channels with an elliptical Gaussian. The conversion between FWHM and Gaussian sigma used is:

$$\sigma = \frac{\mathrm{FWHM}}{2\sqrt{2\ln 2}} \approx \frac{\mathrm{FWHM}}{2.355}$$

This relation is used when creating a Gaussian2DKernel for convolution.

Installation

Assuming you have published the package to PyPI, the simplest install is:

pip install GalCubeCraft

Installing from source (developer mode):

git clone https://github.com/arnablahiry/GalCubeCraft.git
cd GalCubeCraft
pip install -e .

Recommended dependencies are listed in requirements.txt. A minimal set used by the package includes:

• numpy
• scipy
• matplotlib
• astropy
• torch

If you rely on plotting or dendrograms, also ensure astrodendro is available:

pip install astrodendro

Note: for environments with GPU-accelerated PyTorch, install a matching torch build according to your CUDA version (see https://pytorch.org).

Quick start examples

Below are short, runnable examples that demonstrate common workflows. The examples assume a Python session or script; replace package name with the one you published to PyPI if different.

1) Generate one synthetic cube and inspect shapes

from GalCubeCraft import GalCubeCraft

# Create a generator: one cube, grid 125x125, 40 spectral channels (internally oversampled)
g = GalCubeCraft(n_gals=None, n_cubes=1, resolution='all', grid_size=125, n_spectral_slices=40, seed=42)

# Run the generation pipeline and collect results
results = g.generate_cubes()

# Each element in results is a tuple (spectral_cube, params_dict)

cube, params_dict = results[0]
print('cube shape (nz, ny, nx) =', cube.shape)
print('geenration parameter keys =', list(params_dict.keys()))

Typical output:

• cube.shape → (n_velocity, ny, nx) (e.g. (40, 125, 125))
• params_dict contains average_vels, beam_info, pix_spatial_scale, etc.

2) Save and visualise

GalCubeCraft saves generated cubes to data/raw_data/<nz>x<ny>x<nx>/cube_*.npy by default. The class also exposes a visualise helper that wraps the plotting helpers in visualise.py:

g.visualise(results, idx=0, save=False)

This will show moment-0 and moment-1 maps and a velocity spectrum using matplotlib. Set save=True to write PDF figures in figures/<shape>/.

Use as a coarse dataset for transfer learning

GalCubeCraft is intentionally fast, controllable, and able to produce large numbers of cubes with varied orientations, resolutions, surface-brightness scalings and noise behaviour. For these reasons it makes a robust coarse dataset to pretrain machine-learning models before fine-tuning on smaller, scientifically complex datasets.

Recommended workflow:

• Pretrain on large GalCubeCraft datasets to learn general spectro-spatial features (correlated spectral lines, beam-smearing patterns, moment-map structure). Vary resolution, S/N, Sérsic index and component multiplicity to expose the model to a broad prior.
• Fine-tune on a much smaller but higher-fidelity dataset that explicitly includes the morphological complexities your downstream task requires — for example gravitational lensing distortions, diffuse low-surface-brightness emission, bridges and tidal tails from interacting systems, multi-component kinematics, or instrument-specific artifacts.

Why this helps:

• Reduces overfitting to small labelled sets by learning lower-level features on the synthetic data and adapting higher-level representations to the target domain during fine-tuning.
• Speeds training and improves sample efficiency when real or high-fidelity labels are scarce or expensive to create.

Practical tips:

• Freeze early convolutional layers (or set a low learning rate) during initial fine-tuning to preserve general features learned from GalCubeCraft.
• Use domain adaptation techniques (data augmentation, style transfer, or adversarial domain adaptation) to close the gap between synthetic and real observations.
• When you need morphological realism (lensing, bridges, tails, diffuse emission), either augment GalCubeCraft procedurally (apply lensing transforms, add low-surface-brightness components, overlay tidal bridges) or fine-tune on simulation/observation datasets that include such complexity.

Example tasks that benefit from this workflow: denoising, deconvolution, source detection/segmentation, kinematic parameter regression, and anomaly detection in spectral cubes.

Minimal API reference

• GalCubeCraft(n_gals=None, n_cubes=1, resolution='all', offset_gals=5, beam_info=[4,4,0], grid_size=125, n_spectral_slices=40, fname=None, verbose=True, seed=None)
  • Construct the generator. See code docstrings for parameter meanings.
• generate_cubes() → runs the pipeline and returns a list of tuples (cube, params)
• visualise(data, idx, save=False, fname_save=None) → wrapper for plotting utilities

Full API documentation (detailed user guide, class/function reference and extended examples) is currently in preparation on ReadTheDocs and will be published at https://galcubecraft.readthedocs.io when ready — coming soon.

Files of interest in the repository:

• src/GalCubeCraft/core.py — main pipeline and GalCubeCraft class
• src/GalCubeCraft/utils.py — beam, convolution and mask helpers
• src/GalCubeCraft/visualise.py — plotting helpers (moment maps, spectra)

Reproducibility, limitations and edge cases

Edge cases and behaviour to be aware of:

• Small effective radii (much smaller than the beam) trigger flux-scaling to avoid vanishing integrated flux; check all_Se and all_Re if results look unusually bright or faint.
• Very small grids or extremely fine spectral oversampling may increase memory use; the code uses modest oversampling (5x) and then bins channels.
• The generator uses a compact analytic rotation curve (not a full mass-model). For physically realistic kinematics beyond toy data, replace the rotation module with your preferred prescription.

Suggested tests:

• Verify generate_cubes() returns arrays with non-negative flux (the code clips negative numerical artifacts to zero before convolution).
• Confirm beam convolution preserves integrated flux (up to numerical noise).

Troubleshooting

• Import error after pip install: check that PYTHONPATH is not shadowing the installed package and that you're using the same Python interpreter where pip installed the package (use python -m pip install ... to be explicit).
• If plotting fails, ensure GUI backend is available or use a non-interactive backend (e.g., matplotlib.use('Agg')) when running headless.

Credits & citation

This package was developed as a compact educational and research tool for IFU data simulation and denoising algorithm development. If you use GalCubeCraft in published work, please cite the following paper:

Lahiry, A., Díaz-Santos, T., Starck, J.-L., Roy, N. C., Anglés-Alcázar, D., Tsagkatakis, G., & Tsakalides, P.
Deep and Sparse Denoising Benchmarks for Spectral Data Cubes of High-z Galaxies: From Simulations to ALMA Observations.
Submitted to Astronomy & Astrophysics (A&A), 2025.

License: MIT — see the LICENSE file in this repository for the full text.