Karabo constructs and executes radio astronomy data processing and simulation workflows in interactive environments such as Jupyter Notebooks. The package facilitates development, validation, and benchmarking of radio telescope processing methods for projects such as the Square Kilometer Array. It integrates existing tools for interferometer simulation, imaging, and source detection, enabling users to build custom pipelines that simulate instrument behavior, generate synthetic visibilities, perform imaging, and evaluate results with readily available modules. The code supports simulation of general interferometer observations, configuration and visualization of telescope models, and scriptable workflows for tasks such as SRCNet data generation and line emission studies. Karabo includes utilities for parallel processing and data handling, and its extensible design allows combination of third-party libraries and user code to prototype and benchmark radio astronomy algorithms.

pyC²Ray updates C²-Ray (ascl:2312.022), an astrophysical radiative transfer code used to simulate the Epoch of Reionization (EoR). pyC²Ray includes a new raytracing method, ASORA, developed for GPUs, and provides a Python interface for customizable use of the code. The core features of C²-Ray, written in Fortran90, are wrapped using f2py as a Python extension module, while the raytracing library ASORA is implemented in C++ using CUDA. Both are native Python C-extensions and can be directly accessed from any Python script.

C²-Ray3Dm performs time-dependent photo-ionization calculations for 3D multiple sources, and for hydrogen only. Based on C²-Ray (ascl:2312.022), it runs under both MPI and OpenMP. The length of subroutines has been reduced to make the code more manageable and easier to read.

SubgridClumping derives the parameters for the global, in-homogeneous and stochastic clumping model and then computes the clumping factor for large low-resolution N-body simulations smoothed on a regular grid. Written for the CUBEP3M simulation, the package contains two main modules. The first derives the three clumping model parameters for a given small high-resolution simulation; the second computes a clumping factor cube (same mesh-size as input) for the three models for the given density field of a large low-resolution simulation.

PINION (Physics-Informed neural Network for reIONization) predicts the complete 4-D hydrogen fraction evolution from the smoothed gas and mass density fields from pre-computed N-body simulations. Trained on C²-Ray simulation outputs with a physics constraint on the reionization chemistry equation, PINION accurately predicts the entire reionization history between z = 6 and 12 with only five redshift snapshots and a propagation mask as a simplistic approximation of the ionizing photon mean free path. The network's predictions are in good agreement with simulation to redshift z > 7, though the oversimplified propagation mask degrades the network's accuracy for z < 7.