capivara
Capivara: A Spectral-based Segmentation Method for IFU Data Cubes
Capivara implements a spectral-based segmentation method for Integral Field Unit (IFU) data cubes. Designed with astronomers in mind, it facilitates the decomposition of spectral data into regions of similar physical properties, leveraging advanced matrix operations via torch for GPU acceleration.
Installation
Install Capivara from GitHub using the following commands:
install.packages('remotes')
remotes::install_github("RafaelSdeSouza/capivara")
library(capivara)
Usage
Basic Usage: MaNGA Data Cube
This example demonstrates how to use capivara to process an IFU datacube from the MaNGA survey:
require(capivara)
# Read the MaNGA datacube
cube <- "manga-7443-12703-LOGCUBE.fits"
# Apply Capivara segmentation
res <- capivara::segment(cube,Ncomp=20)
# Plot the segmented region
plot <- plot_cluster(res)
print(plot)
Dependencies
- torch: Efficient tensor computations with GPU support.
- ggplot2: Visualization.
- FITSio: Reading and handling FITS files.
- reshape2: Data manipulation.
References
- MaNGA Survey: Bundy, Kevin, et al. “Overview of the SDSS-IV MaNGA Survey: Mapping Nearby Galaxies at Apache Point Observatory.” The Astrophysical Journal 798.1 (2015): 7. DOI: 10.1088/0004-637X/798/1/7
- Capivara Methodology: Souza, Rafael S. de, et al. “Capivara: A Spectral-Based Segmentation Method for IFU Data Cubes.” arXiv preprint (2024). DOI: 10.48550/arXiv.2410.21962
- Torch in R: Paszke, Adam, et al. “PyTorch: An Imperative Style, High-Performance Deep Learning Library.” Advances in Neural Information Processing Systems. 2019.
For more information, check the Capivara GitHub webpage.