N.E.M.O. [Non-stationary Extraction via Multiscale Optical-flow]#
NEMO is a Python pipeline for detecting and tracking compact emission sources across the spectral axis of 3-D radio interferometric data cubes (FITS, HDF5, NumPy). It combines a multiscale starlet wavelet detector with TV-L1 optical flow tracking, kinematic classification, and a dual-metric false-detection filter.
Install NEMO and run the pipeline on your first cube.
Starlet wavelet detection, masked optical flow, track linking, and false-detection removal — explained with equations.
Load cubes, tune parameters, and run the full pipeline without writing any code. Covers the four-card workspace, all viewer windows, and analysis tools.
Application to ALMA [C II] observations of a hyper-luminous quasar at z = 4.6.
Auto-generated reference for all public classes and functions.
Command-line tools for detection, tracking, and denoising.