lensitbiases is an rFFT-based N1 lensing bias calculation and tests. It is tuned for TT, P-only or MV (GMV) like quadratic estimators. It performs rFFT-based N1 and N1 matrix calculations in ~ O(ms) time per lensing multipole for Planck-like config, which allows on-the-fly evaluation of the bias. It also calculates 5 rFFT's of moderate size per L for N1 TT, 20 for PP, and 45 for MV or GMV. lensitbiases is not particularly efficient for low lensing L's, since in this case one must use large boxes.

DIRTY (DustI Radiative Transfer, Yeah!) computes the radiative transfer and dust emission from arbitrary distributions of dust illuminated by arbitrary distributions of sources (usually stars). It uses Monte Carlo methods to solve the radiative transfer problem in full 3D including non-equilibrium and equilibrium thermal dust emission. As are other similar models, DUSTY is computationally intensive; as a result, it is written in C++.

solar-vSI performs Monte Carlo integration of multi-body phase space efficiently. The calculation of solar antineutrino spectra from 8B decay requires the integration of five-body phase space. Though there is no simple analytical approach to this problem, recursive relations can be used to facilitate numerical evaluations.

measure_extinction measures extinction due to dust absorbing photons or scattering photons out of the line-of-sight. Extinction applies to the case for a star seen behind a foreground screen of dust. This package provides the tools to measure dust extinction curves using observations of two effectively identical stars, differing only in that one is seen through more dust than the other.

Forcepho infers the fluxes and shapes of galaxies from astronomical images. It models the appearance of multiple sources in multiple bands simultaneously and compares to observed data via a likelihood function. Gradients of this likelihood allow for efficient maximization of the posterior probability or sampling of the posterior probability distribution via Hamiltonian Monte Carlo. The model intrinsic galaxy shapes and positions are shared across the different bands, but the fluxes are fit separately for each band. Forcepho does not perform detection; initial locations and (very rough) parameter estimates must be supplied by the user.

BayeSED implements full Bayesian interpretation of spectral energy distributions (SEDs) of galaxies and AGNs. It performs Bayesian parameter estimation using posteriori probability distributions (PDFs) and Bayesian SED model comparison using Bayesian evidence. Its latest version BayeSED3 supports various built-in SED models and can emulate other SED models using machine learning techniques.

iPIC3D performs kinetic plasma simulations at magnetohydrodynamics time scales. This three-dimensional parallel code uses the implicit Particle-in-Cell method; implicit integration in time of the Vlasov–Maxwell system removes the numerical stability constraints. Written in C++, iPIC3D can be run with CUDA acceleration and supports MPI, OpenMP, and multi-node multi-GPU simulations.

vortex-p analyzes the velocity fields of astrophysical simulations of different natures (for example, SPH, moving-mesh, and meshless) usually spanning many orders of magnitude in scales involved. The code performs Helmholtz-Hodge decomposition (HHD); that is, it can decompose the velocity field into a solenoidal and an irrotational/compressive part Helmholtz-Hodge decomposition. vortex-p internally uses an AMR representation of the velocity field and can, in principle, capture the full dynamical range of the simulation. The package can also perform Reynolds decomposition (i.e., the decomposition of the velocity field into a bulk and a turbulent part). This is achieved by means of a multi-scale filtering of the velocity field, where the filtering scale around each point is determined by the local flow properties. vortex-p expands the vortex (ascl:2206.001) code, which had been coupled to the outputs of the MASCLET code, to a fully stand-alone tool capable of working with the outcomes of a broad range of simulation methods.

pysymlog provides utilities for binning, normalizing colors, wrangling tick marks, and other tasks, in symmetric logarithm space. For numbers spanning positive and negative values, the code works in log scale with a transition through zero, down to some threshold. This is useful for representing data that span many scales such as standard log-space that include values of zero or even negative values. pysymlog provides convenient functions for creating 1D and 2D histograms and symmetric log bins, generating logspace-like arrays through zero and managing matplotlib major and minor ticks in symlog space, as well as bringing symmetric log scaling functionality to plotly.

This paper introduces RadioSunPy, an open-source Python package developed for accessing, visualizing, and analyzing multi-band radio observations of the Sun from the RATAN-600 solar complex. The advancement of observational technologies and software for processing and visualizing spectro-polarimetric microwave data obtained with the RATAN-600 radio telescope opens new opportunities for studying the physical characteristics of solar plasma at the levels of the chromosphere and corona. These levels remain some difficult to detect in the ultraviolet and X-ray ranges. The development of these methods allows for more precise investigation of the fine structure and dynamics of the solar atmosphere, thereby deepening our understanding of the processes occurring in these layers. The obtained data also can be utilized for diagnosing solar plasma and forecasting solar activity. However, using RATAN-600 data requires extensive data processing and familiarity with the RATAN-600. The package offers comprehensive data processing functionalities, including direct access to raw data, essential processing steps such as calibration and quiet Sun normalization, and tools for analyzing solar activity. This includes automatic detection of local sources, identifying them with NOAA (National Oceanic and Atmospheric Administration) active regions, and further determining parameters for local sources and active regions. By streamlining data processing workflows, RadioSunPy enables researchers to investigate the fine structure and dynamics of the solar atmosphere more efficiently, contributing to advancements in solar physics and space weather forecasting.