This paper introduces an astronomical image alignment algorithm. This algorithm uses the means of the rows and columns of the original image for alignment, and finds the optimal offset corresponding to the maximum similarity by comparing different offsets between images. The similarity is evaluated by the standard deviation of the quotient divided by the means. This paper also discusses the theoretical feasibility of this algorithm. Through practical testing, it has been confirmed that the algorithm is fast and robust.

PIPE (PSF Imagette Photometric Extraction) extracts PSF (point-spread function) photometry from data acquired by the space telescope CHEOPS (CHaracterisation of ExOPlanetS). Advantages of PSF photometry over standard aperture photometry include reduced sensitivity to contaminants such as background stars, cosmic ray hits, and hot/bad pixels. For CHEOPS, an additional advantage is that photometry can be extracted from an imagette, a small window around the target that is downlinked at a shorter cadence than the larger-sized subarray used for aperture photometry. These advantages make PIPE particularly well suited for targets brighter or fainter than the nominal G = 7-11 mag range of CHEOPS, i.e. where short-cadence imagettes are available (bright end) or when contamination becomes a problem (faint end). Within the nominal range, PIPE usually offers no advantage over the standard aperture photometry.

This package provides some utilities for binning, normalizing colors, wrangling tick marks, and more, in symmetric logarithm space. That is, for numbers spanning positive and negative values, working in log scale with a transition through zero, down to some threshold. This can be quite useful for representing data that span many scales like standard log-space, but that include values of zero (that might be returned by physical measurement) or even negative values (for example offsets from some reference, or things like temperatures). This package 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.

Obsplanning is a suite of tools to help plan astronomical observations from ground-based observatories, for traditional single-site as well as multi-station (VLBI) observing. Conveniently determine observability of objects in the sky from your observatory, and produce plots to help you prepare for your observations over the course of a session. Celestial source coordinates (including solar system objects) can be queried or created, and transformed. Calibrator or reference sources can be selected by proximity, and slew order can be optimized to save valuable telescope time. Plots and visualizations can be easily made to chart source elevation and transits, source proximity to the Sun and Moon, concurrent 'up time' of sources at multiple sites (for VLBI or tandem observations), 'dark time' at a telescope site for a given year, finder plots made from real images (with options to query online databases), and more.

The DensityFieldTools toolset manipulates density fields and measures power spectra and bispectra using a very simple interface. After loading a density field, it computes the power spectrum and the bispectrum for a desired binning. The bispectrum estimator also automatically computes the power spectrum for the chosen binning, to facilitate, for example, shot-noise subtraction. DensityFieldTools also provides a quick way to measure (cross-)power spectra directly from density fields.

CLASS-PT modifies the CLASS (ascl:1106.020) code to compute the non-linear power spectra of dark matter and biased tracers in one-loop cosmological perturbation theory, for both Gaussian and non-Gaussian initial conditions. CLASS-PT can be interfaced with the MCMC sampler MontePython (ascl:1805.027) using the (new and improved) custom-built likelihoods found here.

OneLoopBispectrum computes the one-loop bispectrum of galaxies in redshift space. It computes and simplifies the bispectrum kernels using Mathematica; this is cosmology-independent. The code also computes the full and flattened bispectrum templates, given the pre-computed integration kernels. OneLoopBispectrum uses Mathematica to read in and combine the bispectrum templates, and Python to interpolate and extract the one-loop bispectrum.

URecon reconstructs the initial conditions of N-body simulations from late time (e.g., z=0) density fields. This simple UNET architecture is implemented in TensorFlow and requires Pylians3 (ascl:2403.012) for measuring power spectrum of density fields. The package includes weights trained on Quijote fiducial cosmology simulations.

Pylians3 (Python3 libraries for the analysis of numerical simulations) provides a Python 3 version of Pylians (ascl:1811.008), which analyzes numerical simulations (both N-body and hydrodynamic); parts of the codebase are also written in cython and C. It computes density fields, power spectra, bispectra, and correlation functions, identifies voids, and populates halos with galaxies using an HOD. Pylians3 also applies HI+H2 corrections to the output of hydrodynamic simulations, make 21cm maps, computes DLAs column density distribution functions, and can plot density fields and make movies.

LtU-ILI (Learning the Universe Implicit Likelihood Inference) performs machine learning parameter inference. Given labeled training data or a stochastic simulator, the LtU-ILI piepline automatically trains state-of-the-art neural networks to learn the data-parameter relationship and produces robust, well-calibrated posterior inference. The package comes with a wide range of customizable complexity, including posterior-, likelihood-, and ratio-estimation methods for ILI, including sequential learning analogs, and various neural density estimators, including mixture density networks, conditional normalizing flows, and ResNet-like ratio classifiers. It offers fully-customizable, exotic embedding networks, including CNNs and Graph Neural Networks, and a unified interface for multiple ILI backends such as sbi, pydelfi, and lampe. LtU-ILI also handles multiple marginal and multivariate posterior coverage metrics, and offers Jupyter and command-line interfaces and a parallelizable configuration framework for efficient hyperparameter tuning and production runs.

FitCov estimates the covariance of two-point correlation functions in a way that requires fewer mocks than the standard mock-based covariance. Rather than using an analytically fixed correction to some terms that enter the jackknife covariance matrix, the code fits the correction to a mock-based covariance obtained from a small number of mocks. The fitted jackknife covariance remains unbiased, an improvement over other methods, performs well both in terms of precision (unbiased constraints) and accuracy (similar uncertainties), and requires significant less computational power. In addition, FitCov can be easily implemented on top of the standard jackknife covariance computation.

pycorr wraps two-point counter engines such as Corrfunc (ascl:1703.003) to estimate the correlation function. It supports theta (angular), s, s-mu, rp-pi binning schemes, analytical two-point counts with periodic boundary conditions, and inverse bitwise weights (in any integer format) and (angular) upweighting. It also provides MPI parallelization and jackknife estimate of the correlation function covariance matrix.

s4cmb (Systematics For Cosmic Microwave Background) studies the impact of instrumental systematic effects on measurements of CMB experiments based on bolometric detector technology. s4cmb provides a unified framework to simulate raw data streams in the time domain (TODs) acquired by CMB experiments scanning the sky, and to inject in these realistic instrumental systematics effect.

The MINDS hybrid pipeline is based on the JWST pipeline and routines from the VIP package (ascl:1603.003) for the reduction of JWST MIRI-MRS data. The pipeline compensates for some of the known weaknesses of the official JWST pipeline to improve the quality of spectrum extracted from MIRI-MRS data. This is done by leveraging the capabilities of VIP, another large data reduction package used in the field of high-contrast imaging.

The front end of the pipeline is a highly automated Jupyter notebook. Parameters are typically set in one cell at the beginning of the notebook, and the rest of the notebook can be run without any further modification. The Jupyter notebook format provides flexibility, enhanced visibility of intermediate and final results, more straightforward troubleshooting, and the possibility to easily incorporate additional codes by the user to further analyze or exploit their results.

fkpt computes the 1-loop redshift space power spectrum for tracers using perturbation theory for LCDM and Modified Gravity theories using "fk"-Kernels. Though implemented for the Hu-Sawicky f(R) modified gravity model, it is straightforward to use it for other models.

Poke (pronounced /poʊˈkeɪ/ or po-kay) uses commercial ray tracing APIs and open-source physical optics engines to simultaneously model scalar wavefront error, diffraction, and polarization to bridge the gap between ray trace models and diffraction models. It operates by storing ray data from a commercial ray tracing engine into a Python object, from which physical optics calculations can be made. Poke provides two propagation physics modules, Gaussian Beamlet Decomposition and Polarization Ray Tracing, that add to the utility of existing scalar diffraction models. Gaussian Beamlet Decomposition is a ray-based approach to diffraction modeling that integrates physical optics models with ray trace models to directly capture the influence of ray aberrations in diffraction simulations. Polarization Ray Tracing is a ray-based method of vector field propagation that can diagnose the polarization aberrations in optical systems.

BTSbot automates real-time identification of bright extragalactic transients in Zwicky Transient Facility (ZTF) data. A multi-modal convolutional neural network, BTSbot provides a bright transient score to individual ZTF detections using their image data and 25 extracted features. The package eliminates the need for daily visual inspection of new transients by automatically identifying and requesting spectroscopic follow-up observations of new bright transient candidates. BTSbot recovers all bright transients in our test split and performs on par with human experts in terms of identification speed (on average, ∼1 hour quicker than scanners).

kinematic_scaleheight uses MCMC methods to kinematically estimate the vertical distribution of clouds in the Galactic plane, including the least squares analysis of Crovisier (1978), an updated least squares analysis using a modern Galactic rotation model, and a Bayesian model sampled via MCMC as described in Wenger et al. (2024).

DistClassiPy uses different distance metrics to classify objects such as light curves. It provides state-of-the-art performance for time-domain astronomy, and offers lower computational requirements and improved interpretability over traditional methods such as Random Forests, making it suitable for large datasets. DistClassiPy allows fine-tuning based on scientific objectives by selecting appropriate distance metrics and features, which enhances its performance and improves classification interpretability.

Pynkowski computes Minkowski Functionals and other higher order statistics of input fields, as well as their expected values for different kinds of fields. This package supports Minkowski functionals, and maxima and minima distributions. Supported input formats include scalar HEALPix maps such as those used by healpy (ascl:2008.022) and polarization HEALPix maps in the SO(3) formalism. Pynkowski also supports various theoretical fields, including Gaussian (e.g., CMB Temperature or the initial density field), Chi squared (e.g., CMB polarization intensity), and spin 2 maps in the SO(3) formalism.

KCWIKit extends the official KCWI DRP (ascl:2301.019) with a variety of stacking tools and DRP improvements. The software offers masking and median filtering scripts to be used while running the KCWI DRP, and a step-by-step KCWI_DRP implementation for finer control over the reduction process. Once the DRP has finished, KCWIKit can be used to stack the output cubes via the Montage package. Various functions cross-correlate and mosaic the constituent cubes and the final stacked cubes are WCS corrected. Helper functions can then be used to deproject the stacked cube into lower-dimensional representations should the user desire.

cbeam is a Python/Julia package which models the propagation of guided light through slowly-varying few-mode waveguides using the coupled-mode theory (CMT). When compared with more general numerical methods for waveguide simulation, such as the finite-differences beam propagation method (FD-BPM), numerical implementations of the CMT can be much more computationally efficient. cbeam also provides a Pythonic class structure to define waveguides, with simple classes for directional couplers and photonic lanterns already provided. Finally, cbeam doubles as a finite-element eigenmode solver.

2cosmos is a modification of Monte Python (ascl:1307.002) and allows the user to write likelihood modules that can request two independent instances of CLASS (ascl:1106.020) and separate dictionaries and structures for all cosmological and nuisance parameters. The intention is to be able to evaluate two independent cosmological calculations and their respective parameters within the same likelihood. This is useful for evaluating a likelihood using correlated datasets (e.g. mutually exclusive subsets of the same dataset for which one wants to take into account all correlations between the subsets).

SkyLine generates mock line-intensity maps (both in 3D and 2D) in a lightcone from a halo catalog, accounting for the evolution of clustering and astrophysical properties, and observational effects such as spectral and angular resolutions, line-interlopers, and galactic foregrounds. Using a given astrophysical model for the luminosity of each line, the code paints the signal for each emitter and generates the map, adding coherently all contributions of interest. In addition, SkyLine can generate maps with the distribution of Luminous Red Galaxies and Emitting Line Galaxies.

star_shadow automatically analyzes space based light curves of eclipsing binaries and provide a measurement of eccentricity, among other parameters. It measures the timings of eclipses using the time derivatives of the light curves, using a model of orbital harmonics obtained from an initial iterative prewhitening of sinusoids. Since the algorithm extracts the harmonics from the rest of the sinusoidal variability eclipse timings can be measured even in the presence of other (astrophysical) signals, thus determining the orbital eccentricity automatically from the light curve along with information about the other variability present in the light curve. The output includes, but is not limited to, a sinusoid plus linear model of the light curve, the orbital period, the eccentricity, argument of periastron, and inclination.

ECLIPSR fully and automatically analyzes space based light curves to find eclipsing binaries and provide some first order measurements, such as the binary star period and eclipse depths. It provides a recipe to find individual eclipses using the time derivatives of the light curves, including eclipses in light curves of stars where the dominating variability is, for example, pulsations. Since the algorithm detects each eclipse individually, even light curves containing only one eclipse can (in principle) be successfully analyzed and classified. ECLIPSR can find eclipsing binaries among both pulsating and non-pulsating stars in a homogeneous and quick manner and process large amounts of light curves in reasonable amounts of time. The output includes, among other things, the individual eclipse markers, the period and time of first (primary) eclipse, and a score between 0 and 1 indicating the likelihood that the analyzed light curve is that of an eclipsing binary.

polarizationtools converts, analyzes, and simulates polarization data. The different python scripts (1) convert Stokes parameters into linear polarization parameters with proper treatment of the uncertainties and vice versa; (2) shift electric vector position angle (EVPA) data points in time series to account for the 180 degrees ambiguity; (3) identify rotations of the EVPA e.g. in blazar polarization monitoring data according to various rotation definitions; and (4) simulate polarization time series as a random walk in the Stokes Q-U plane.

The TAT-pulsar (Timing Analysis Toolkit for Pulsars) package is a specialized toolkit designed for handling the scientific intricacies of pulsar timing. It provides a suite of Python-based utilities and scripts that facilitate the analysis, processing, and visualization of pulsar data. By leveraging observational data from pulsars, along with the associated physical processes and statistical characteristics, TAT-pulsar integrates a series of useful tools and data analysis scripts specifically developed for both isolated pulsars and binary systems. This enables swift analysis and the detailed presentation of timing properties in the high-energy pulsar field. Developed and implemented completely independently from other pulsar timing software such as Stingray (ascl:1608.001) and PINT (ascl:1902.007), TAT-pulsar serves as a valuable cross-checking and supplementary tool for data analysis.

We introduce GalMOSS, a Python-based, Torch-powered tool for two-dimensional fitting of galaxy profiles. By seamlessly enabling GPU parallelization, GalMOSS meets the high computational demands of large-scale galaxy surveys, placing galaxy profile fitting in the LSST-era. It incorporates widely used profiles such as the Sérsic, Exponential disk, Ferrer, King, Gaussian, and Moffat profiles, and allows for the easy integration of more complex models. Tested on 8,289 galaxies from the Sloan Digital Sky Survey (SDSS) g-band with a single NVIDIA A100 GPU, GalMOSS completed classical Sérsic profile fitting in about 10 minutes. Benchmark tests show that GalMOSS achieves computational speeds that are 6 $\times$ faster than those of default implementations.

MGPT (Modified Gravity Perturbation Theory) computes 2-point statistics for LCDM model, DGP and Hu-Sawicky f(R) gravity. Written in C, the code can be easily modified to include other models. Specifically, it computes the SPT matter power spectrum, SPT Lagrangian-biased tracers power spectrum, and the CLPT matter correlation function. MGPT also computes the CLPT Lagrangian-biased tracers correlation function and a set of Q and R functionsfrom which other statistics, as leading order bispectrum, can be constructed.

CCBH-Numerics (previously called CCBH-PLPP) computes the probability of the existence of a single cosmologically coupled black hole (BH) with a formation mass below a specified threshold for given observational data of binary black holes (BBHs) from gravitational waves. The code uses the unbiased population of BBHs, as given by the power-law-plus-peak (PLPP) profile, as the observational input, and assumes that the detected BBHs are formed from stellar evolution, not primordial BHs. CCBH-Numerics also works with individual data from BBHs and for NSBH pairs as well.

Rwcs offers access to all the projection and distortion systems of WCSLIB (ascl:1108.003) in R. This can be used directly for, for example, pixel lookups, or for higher level general distortion and projection.

Rfits reads and writes FITS images, tables, and headers. Written in R, Rfits works with all types of compressed images, and both ASCII and binary tables. It uses CFITSIO (ascl:1010.001) for all low level FITS IO, so in general should be as fast as other CFITSIO-based software. For images, Rfits offers fully featured memory mapping and on-the-fly subsetting (by pixel and coordinate) and sparse pixel sampling, allowing for efficient inspection of very large (larger than memory) images.

NMMA probes nuclear physics and cosmology with multimessenger analysis. This fully featured, Bayesian multi-messenger pipeline targets joint analyses of gravitational-wave and electromagnetic data (focusing on the optical). Using bilby (ascl:1901.011) as the back-end, the software uses a variety of samplers to sampling these data sets. NMMA uses chiral effective field theory based neutron star equation of states when performing inference, and is also capable of estimating the Hubble Constant.

escatter.py performs Monte Carlo simulations of electron scattering events. The code was developed to better understand the emission lines from the interacting supernova SN 2021adxl, specifically the blue excess seen in the Hα 6563A emission line. escatter follows a photon that was formed in a thin interface between the supernova ejecta and surrounding material as it travels radially outwards through the dense material, scattering electrons outwards until it reaches an optically thin region, and plots a histogram of the emergent photons.

StructureFunction determines the X-ray Structure Function of a population of Active Galactic Nuclei (AGN) for which two epoch X-ray observations are available and are separated by rest frame time interval. The calculation of the X-ray structure function is Bayesian. The sampling of the likelihood uses Stan (ascl:1801.003) for statistical modeling and high-performance statistical computation.

tidalspin uses a Bayesian approach to infer posterior distributions of a black hole's parameters (mass and spin) in an observed tidal disruption event, given a prior estimate of the black hole’s mass (e.g., from a galactic scaling relationship, or the tidal disruption event’s observed properties). These posterior distributions will only utilize the properties of tidal forces in their inference. tidalspin can be applied to the population of tidal disruption events already discovered.

QuantifAI reconstructs radio interferometric images using scalable Bayesian uncertainty quantification relying on data-driven (learned) priors. It relies on the convex accelerated optimization algorithms in CRR (ascl:2401.016) and is built on top of PyTorch. QuantifAI also includes MCMC algorithms for posterior sampling.

CRR (Convex Ridge Regularizer) takes the gradient of regularizers that are the sum of convex-ridge functions and parameterizes them using a neural network that has a single hidden layer with increasing and learnable activation functions. The neural network is trained within a few minutes as a multistep Gaussian denoiser, and offers improvements for denoising and image reconstruction over other methods with similar reliability.

maskfill inward extrapolates edge pixels just outside masked regions, using iterative median filtering and the full information contained in the edge pixels. This provides seamless transitions between masked pixels and good pixels, and allows high fidelity reconstruction of gaps in continuous narrow features. An image and a mask the only required inputs.

LoRD (Locate Reconnection Distribution) identifies the locations and structures of 3D magnetic reconnection within discrete magnetic field data. The toolkit contains three main functions; the first, ARD (Analyze Reconnection Distribution) locates the grids undergoing reconnection without null points and also recognizes the local configurations of reconnection sites. ANP (Analyze Null Points) locates and classifies the 3D null points, and APNP (Analyze Projected Null Points) analyzes the 2D neutral points projected on a plane near a cell. LoRD is written in Matlab and the toolkit contains demo scripts.

SolarKAT mitigates solar interference in MeerKAT data and recovers the visibilities rather than discarding them; this solar imaging pipeline takes 1GC calibrated data in Measurement Set format as input. Written in Python, the pipeline employs solar tracking, subtraction, and peeling techniques to enhance data quality by significantly reducing solar radio interference. This is achieved while preserving the flux measurements in the main field. SolarKAT is versatile and can be applied to general radio astronomy observations and solar radio astronomy; additionally, generated solar images can be used for weather forecasting. SolarKAT is deployed in Stimela (ascl:2305.007). It is based on existing radio astronomy software, including CASA (ascl:1107.013), breizorro (ascl:2305.009), WSclean (ascl:1408.023), Quartical (ascl:2305.006), and Astropy (ascl:1304.002).

baryon-sweep produces a robust outlier rejection while simultaneously preserving the signal of the science target. The code works as a standalone solution or as a supplement to the current pipeline software. baryon-sweep creates the 2D pixel mask and mask layers, processes the sky (non-science target) spaxels, and creates a post-processed cube ready for use.

Ostrich emulates surrogate models for complex and expensive functions using Principal Component Analysis (PCA) to decompose a signal, then interpolate the PCA weights over the parameters θ using a Gaussian Process interpolator. The code is trained on samples from the expensive functions, recreating and interpolating between those training samples with reduced computational cost, and recalculating for each use.

The Feed Forward Neural Network SYSNet models the relationship between the imaging maps, such as stellar density and the observed galaxy density field, in order to mitigate the systematic effects and to make a robust galaxy clustering measurements. The cost function is Mean Squared Error and a L2 regularization term, and the optimization algorithm is Adaptive Moment (ADAM).

harmonic learns an approximate harmonic mean estimator (referred to as a "learnt harmonic mean estimator") from posterior distribution samples to compute the marginal likelihood required for Bayesian model selection. Using a large number of independent Markov chain Monte Carlo (MCMC) chains from another package such as emcee (ascl:1303.002), harmonic uses importance sampling to learn a new target distribution in order to optimize an approximate harmonic estimator while minimizing its variance.

DARC (Dirac Atomic R-matrix Codes) enables the study of continuum processes for a general atomic system. The suite of programs calculate electron-atom or electron-ion collision cross-sections. In addition, the programs include code for bound-state and photoionization calculations.

deal.II computes solutions to partial differential equations (PDEs) using adaptive finite elements. The code provides an interface for processing PDEs accessible to both laptops and supercomputers, and has been used to investigate the local and global waveform effects of gravitational waves by numerical simulation. deal.II supports massively parallel computing of very large linear systems of equations and provides access to triangulation of various geometries of the simulation domain.

LoSoTo (LOFAR Solution Tool) performs a variety of operations on H5parm data, which is based on the HDF5 format; it isolates direction independent systematic effects and can therefore be transferred to the target field. Subsets of data can be selected for each operation using lists of axes values, regular expressions, or intervals. The LoSoTo package stores solutions in arrays organized in a hierarchical fashion; this provides flexibility and preserves performance. The code can, for example, extract Faraday rotation from RR/LL phase solutions or a rotation matrix, clip solutions around the median, and calculate the ionospheric structure function. LoSoTo includes an outlier flagging procedure, normalizes solutions to a given value, and offers an advanced plotting routine, and many other operations.

CosmosCanvas creates perception-based color maps for different astrophysical properties such as spectral index and velocity fields. Three tutorials demonstrate how to use python code to exploit and adjust the boundaries in these divergent colour schemes. Intended to work with human physiology, each tutorial offers at least one default scheme that is monotonic in value both as a redundancy for supporting data information and an aid for colour blind viewers. This library relies on Gilles Ferrand's colourspace library.

pyPETAL produces cross-correlation functions, discrete correlation functions, and mean time lags from multi-band AGN time-series data, combining multiple different codes (including pyCCF (ascl:1805.032), pyZDCF, PyROA (ascl:2107.012), and JAVELIN (ascl:1010.007)) used for active galactic nuclei (AGN) reverberation mapping (RM) analysis into a unified pipeline. This pipeline also implements outlier rejection using Damped Random Walk Gaussian process fitting, and detrending through the LinMix algorithm. pyPETAL implements a weighting scheme for all lag-producing modules, mitigating aliasing in peaks of time lag distributions between light curves. pyPETAL scales to any combination of internal code modules, supporting a variety of computational workflows.

LUNA generates dynamically accurate lightcurves from a planet-moon pair, analytically accounting for shadow overlaps, stellar limb darkening, and planet-moon dynamical motion. The code takes transit timing/duration variations and ingress/egress asymmetries into consideration not only for the planet, but also the moon. LUNA was designed to be analytical and dynamical and to incorporate limb darkening (including non-linear laws) and account for all orbital elements, including eccentricity and longitude of the ascending node. Because the software is precise and analytic, LUNA is a highly potent tool for exomoon detection.

The 3-D convection code Rayleigh enables study of dynamo behavior in spherical geometry. It evolves the incompressible and anelastic MHD equations in spherical geometry using a pseudo-spectral approach. Rayleigh employs spherical harmonics in the horizontal direction and Chebyshev polynomials in the radial direction and has undergone extensive accuracy testing.

tomso loads and saves input and output files for and from stellar evolution and oscillation codes. The functions are bundled together in modules that correspond with a specific stellar evolution code, stellar oscillation code, or file format. tomso supports the FGONG format and various input/output files for ADIPLS (ascl:1109.002), GYRE (ascl:1308.010), MESA (ascl:1010.083), and STARS (ascl:1107.008). tomso's main purpose is to provide a compact interface for manipulating input and output data in these formats and simplify research that uses them.

BSAVI (Bayesian Sample Visualizer) is a tool to aid likelihood analysis of model parameters where samples from a distribution in the parameter space are used as inputs to calculate a given observable. For example, selecting a range of samples will allow you to easily see how the observables change as you traverse the sample distribution. At the core of BSAVI is the Observable object, which contains the data for a given observable and instructions for plotting it. It is modular, so you can write your own function that takes the parameter values as inputs, and BSAVI will use it to compute observables on the fly. It also accepts tabular data, so if you have pre-computed observables, simply import them alongside the dataset containing the sample distribution to start visualizing.

NE2001p is a fully Python implementation of the NE2001 Galactic electron density model. NE2001p forward models the dispersion and scattering of compact radio sources, including pulsars, fast radio bursts, AGNs, and masers, and the model predicts the distances of radio sources that lack independent distance measures.

The SubGen2 subhalo population generator works for both CDM and WDM of arbitrary DM particle mass. It can be used to generate a population of subhaloes according to the joint distribution of subhalo bound mass, infall mass and halo-centric distance in a halo of a given mass. SubGen2 is an extension to SubGen (ascl:2312.035), which works only for CDM subhaloes.

SubGen generates Monte-Carlo samples of dark matter subhaloes. It fully describes the joint distribution of subhaloes in final mass, infall mass, and radius; it can be used to predict derived distributions involving combinations of these quantities, including the universal subhalo mass function, the subhalo spatial distribution, the gravitational lensing profile, the dark matter annihilation radiation profile and boost factor. SubGen works only for CDM subhaloes; for an extension of the code to also work with WDM subhaloes, see SubGen2 (ascl:2312.036).

pycheops analyzes CHEOPS light curve data. The models in the package can also be applied to other types of data. pycheops includes a "cook book" and examples; in addition, it provides a command-line tool that aids in the preparation of observing requests for CHEOPS observers.

RADIS resolves spectra with millions of lines within seconds on a single-CPU and can be GPU-accelerated. It supports HITRAN, HITEMP and ExoMol out-of-the-box (auto-download), and therefore is particularly suitable to compute cross-sections or transmission spectra at high-temperature. RADIS includes equilibrium calculations for all species, and non-LTE for CO2 and CO.

gaia_tools contains codes for working with the ESA/Gaia data and related data sets (APOGEE, GALAH, LAMOST DR2, and RAVE). Written in Python, it includes tools to read catalogs, perform cross-matching, read RVS or XP spectra, and query the Gaia archive. gaia_tools also contains various matching recipes, such as matching APOGEE or APOGEE-RC to Gaia DR2, and RAVE to TGAS (taking into account the epoch difference).

AM³ simulates lepto-hadronic interactions in astrophysical environments. It solves the time-dependent partial differential equations for the energy spectra of electrons, positrons, protons, neutrons, photons, neutrinos as well as charged secondaries (pions and muons), immersed in an isotropic magnetic field. The code accounts for the emission of photons and charged secondaries in electromagnetic and hadronic interactions feed back into the interaction rates in a time-dependent manner, therefore grasping non-linear effects including electromagnetic cascades. AM³ is computationally efficient, making it possible to scan vast source parameter scans and fit the observational data, and has been deployed to explain multi-wavelength observations from blazars, gamma-ray bursts and tidal disruption events.

matvis simulates radio interferometric visibilities at the necessary scale with both CPU and GPU implementations. It is matrix-based and applicable to wide field-of-view instruments such as the Hydrogen Epoch of Reionization Array (HERA) and the Square Kilometre Array (SKA), as it does not make any approximations of the visibility integral (such as the flat-sky approximation). The only approximation made is that the sky is a collection of point sources, which is valid for sky models that intrinsically consist of point-sources, but is an approximation for diffuse sky models. The matvix matrix-based algorithm is fast and scales well to large numbers of antennas. The code supports both CPU and GPU implementations as drop-in replacements for each other and also supports both dense and sparse sky models.

RRLFE generates and applies calibrations for retrieving [Fe/H] from low-res spectra of RR Lyrae variable stars. The code can generate a metallicity calibration anew, from real or synthetic spectra; it can also apply a metallicity calibration to low-resolution (R ~2000) RR Lyrae spectra spanning 3911 to 4950 angstroms.

SAGE corrects the time-dependent impact of stellar activity on transmission spectra. It uses a pixelation approach to model the stellar surface with spots and faculae, while accounting for limb-darkening and rotational line-broadening. The code can be used to evaluate stellar contamination for F to M-type hosts, test various spot sizes and locations, and quantify the impact of limb-darkening. SAGE can also retrieve the properties and distribution of active regions on the stellar surface from photometric monitoring, and connect the photometric variability to the stellar contamination of transmission spectra.

galclaim identifies association between astrophysical transient sources and host galaxy. This association is made by estimating the chance alignment between a given transient sky localization and nearby galaxies. The code can be used with various catalogs, including Pan-STARRS, HSC, AllWISE and GLADE. galclaim also pre-checks for nearby bright galaxy using the RC3 catalog (https://heasarc.gsfc.nasa.gov/w3browse/all/rc3.html). When a nearby galaxy is found, a warning is raised and the properties of the galaxy are saved in a dedicated output file. The package can create plots displaying the computed pval for the found objects for each transient and each catalog; plots are stored in the result/plots directory.

CloudFlex models observational signatures associated with the small-scale structure of the circumgalactic medium. It populates cool gas structures in the CGM as a complex of cloudlets using a Monte Carlo method. Various parameters can be set to describe the structure of the cloudlet complexes, including cloudlet mass, density, velocity, and size. Functionality exists for generating the observational signatures of sightlines piercing these cloudlet complexes, borrowing heavily from the Trident code (ascl:1612.019).

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-Ray3Dm1D_Helium is the hydrogen + helium version of the radiative transfer photo-ionization code C²-Ray. It combines the 1D and 3D versions of the code.

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.

C²-Ray calculates spherical symmetric time-dependent photo-ionization in 1D with the source at the origin for hydrogen only. The code is explicitly photon-conserving and uses an analytical relaxation solution for the ionization rate equations for each time step, thus enabling integration of the equation of transfer along a ray with fewer cells and time steps than previous methods. It is suitable for coupling radiative transfer to gas and N-body dynamics methods on fixed or adaptive grids. C²-Ray is not parallelized but contains an MPI module for compatibility with the 3D version (C²-Ray3Dm).

PyRaTE (Python Radiative Transfer Emission) post-processes astrochemical simulations. This multilevel radiative transfer code uses the escape probablity method to calculate the population densities of the species under consideration. The code can handle all projection angles and geometries and can also be used to produce mock observations of the Goldreich-Kylafis effect. PyRaTE is written in Python; it uses a parallel strategy and relies on the YT analysis toolkit (ascl:1011.022), mpi4py and numba.

The ProPane package comes with key utilities for warping between different WCS systems: propaneWarp (for warping individual frames once). ProPane also contains the various functions for creating large stacks of many warped frames (which is of class ProPane, which is roughly meant to suggest the idea of many panes of glass being stacked together). It uses the wcslib C library (ascl:1108.003) for projections (all legal ones are supported) via the Rwcs package, and uses the threaded Cimg C++ library via the imager library to do image warping. ProPane also contains functions converted from older (deprecated) Rwcs and ProFound (ascl:1804.006) related functions.

Rainbow is a black-body parametric model for transient light curves. It uses Bazin function as a model for bolometric flux evolution and a logistic function for the temperature evolution; it provides seven fit parameters and goodness of fit (reduced χ²) and is well-suited for transient objects. Also included is RainbowRisingFit, suitable for rising transient objects, which offers six fit parameters. It is based on a rising sigmoid bolometric flux and a sigmoid temperature evolution. These implementations are implemented in the light-curve processing toolbox (ascl:2107.001) for Python.

PyMsOfa accesses the International Astronomical Union’s SOFA library (ascl:1403.026) from Python. It offers a wrapper package based on a foreign function library for Python (ctypes), a wrapper with the foreign function interface for Python calling C code (cffi), and a package directly written in pure Python codes from SOFA subroutines. PyMsOfa is suitable for the astrometric detection of habitable planets of the Closeby Habitable Exoplanet Survey (CHES) mission and for the frontier themes of black holes and dark matter related to astrometric calculations and other fields.

LimberJack.jl performs cosmological analyses of 2 point auto- and cross-correlation measurements from galaxy clustering, CMB lensing and weak lensing data. Written in Julia, it obtains gradients for its outputs faster than traditional finite difference methods, making the code greatly synergistic with gradient-based sampling methods such as Hamiltonian Monte Carlo. LimberJack.jl can efficiently exploring parameter spaces with hundreds of dimensions.

The Farmer contains photometry routines geared towards deep, multi-wavelength galaxy surveys. It fits simple parametric surface brightness profiles provided by The Tractor (ascl:1604.008) to measure precision photometry even in deeply crowded fields when provided with a suitable high resolution detection image. The Farmer has been used to build a number of galaxy survey catalogs including COSMOS202, SHELA, and H20.

SUNBIRD trains neural-network-based models for galaxy clustering. It also incorporates pre-trained emulators for different summary statistics, including galaxy two-point correlation function, density-split clustering statistics, and old-galaxy cross-correlation function. These models have been trained on mock galaxy catalogs, and were calibrated to work for specific samples of galaxies. SUNBIRD implements routines with PyTorch to train new neural-network emulators.

GRFolres performs simulations in modified theories of gravity. It is based on GRChombo (ascl:2306.039) and inherits all of the capabilities of the main GRChombo code, which makes use of the Chombo library (ascl:1202.008) for adaptive mesh refinement. The code implements the 4∂ST theory of modified gravity and the cubic Horndeski theory in (3+1)-dimensional numerical relativity. GRFolres can be used for stable gauge evolution, solving the modified energy and momentum constraints for initial conditions, and monitoring the constraint violation and calculating the energy densities associated with the different scalar terms in the action. It can also extract data for the tensor and scalar gravitational waveforms.

21cmEMU emulates 21cmFAST (ascl:1102.023) summary statistics, among them the 21-cm power spectrum, 21-cm global brightness temperature, IGM spin temperature, and neutral fraction. It also emulates the Thomson scattering optical depth and UV luminosity functions. With 21cmFAST installed, parameters can be supplied direction to 21cmEMU, and 21cmEMU can be used for, for example, analytic calculations of tau_e and UV luminosity functions. The code is included as an alternative simulator in 21cmMC (ascl:1608.017).

The folding pipeline PulsarX searches for pulsars. The code includes radio frequency interference mitigation, de-dispersion, folding, and parameter optimization, and supports both psrfits and filterbank data formats. The toolset has two implementations of the folding pipelines; one uses a brute-force de-dispersion algorithm, and the other an algorithm that becomes more efficient than the brute-force de-dispersion algorithm as the number of candidates increases. PulsarX is appropriate for large-scale pulsar surveys.

PhotochemPy finds the steady-state chemical composition of an atmosphere or evolves atmospheres through time. Given inputs such as the stellar UV flux and atmospheric temperature structure, the code creates a photochemical model of a planet's atmosphere. PhotochemPy is a distant fork of Atmos (ascl:2106.039). It provides a Python wrapper to Fortran source code but can also be used exclusively in Fortran.

FORECAST generates realistic astronomical images and galaxy surveys by forward modeling the output snapshot of any hydrodynamical cosmological simulation. It exploits the snapshot by constructing a lightcone centered on the observer's position; the code computes the observed fluxes of each simulated stellar element, modeled as a Single Stellar Population (SSP), in any chosen set of pass-band filters, including k-correction, IGM absorption, and dust attenuation. These fluxes are then used to create an image on a grid of pixels, to which observational features such as background noise and PSF blurring can be added. FORECAST provides customizable options for filters, size of the field of view, and survey parameters, thus allowing the synthetic images to be tailored for specific research requirements.

The non-parametric Jeans code GravSphere models discrete data and can be used to model dark matter distributions in galaxies. It can also recover the density ρ(r) and velocity anisotropy β(r) of spherical stellar systems, assuming only that they are in a steady state. Real or mock data are prepared by using the included binulator.py code; the repository also includes many examples for exploring the GravSphere's capabilities.

The CompressedFisher library tests whether Fisher forecasts using simulated components are converged. The library contains tools to compute standard Fisher estimates, estimate the level of bias due to the finite number of simulations, and compute the compressed Fisher information. Typical usage of CompressedFisher requires two ensembles of simulations: one set of simulations is given at the fiducial parameters (𝜃) to estimate the covariance matrix. The second is a set of simulated derivatives; these can either be in the form of realizations of the derivatives themselves or simulations evaluate at a set of point in the neighborhood of the fiducial point that the code can use to estimate the derivatives.

CosmoLED computes Hawking evaporation from black holes and set constraints on the fraction of black holes in dark matter. Based on ExoCLASS (ascl:1106.020), the code provides a DarkAges_LED module and C codes in class_LED to compute the evolution and energy deposition functions from LED black holes. Though CosmoLED is designed for large extra dimension black holes, it can also be used to study 4D black holes.

SolarAxionFlux quantifies systematic differences and statistical uncertainties in the calculation of the solar axion flux from axion-photon and axion-electron interactions. Determining the limitations of these calculations can be used to identify potential improvements and help determine axion model parameters more accurately.

LyaCoLoRe uses CoLoRe (ascl:2111.009) simulations to generate simulated Lyman alpha forest spectra. The code takes the output files from CoLoRe as an input, carries out several stages of processing, and produces realistic skewers of transmitted flux fraction as an output. The repository includes tools to tune the parameters within LyaCoLoRe's transformation, and to measure the 1D power spectrum of output skewers quickly.

DENSe enables Bayesian non-parametric inferences of densities of Poisson data counts. Its framework of stateless methods is written in Python, although it relies on NIFTy (ascl:1302.013, ascl:1903.008) for the heavy lifting. DENSe utilizes all available information in the data by modeling the inherent correlation structure using a Matérn kernel. The inference of the density from count data can be written in a single line of python code. The fitting method takes a multidimensional numpy array as input and returns multidimensional arrays of the same dimensions encoding the density field.

BUQO solves large-scale imaging inverse problems. It leverages probability concentration phenomena and the underlying convex geometry to formulate the Bayesian hypothesis test as a convex problem that is then efficiently solved by using scalable optimization algorithms. This allows scaling to high-resolution and high-sensitivity imaging problems that are computationally unaffordable for other Bayesian computation approaches.

PROSPECT infers cosmological parameters using profile likelihoods. It constructs an approximate profile likelihood from an MCMC and optimizes it using simulated annealing, a gradient-free stochastic optimization algorithm. It employs an automatic tuning of the step size parameter and binned covariance matrices from the MCMC to achieve efficient optimizations of the profile likelihood.

smops interpolates input sub-band model FITS images, such as those produced by WSClean (ascl:1408.023), into more finely channelized sub-band model FITS images, thus generating model images at a higher frequency resolution. It is a Python-based command line tool. For example, given input model FITS images initially created from sub-dividing a given bandwidth into four, smops can subdivide that bandwidth further, resulting in more finely channelized model images, to a specified frequency resolution. This smooths out the stepwise behavior of models across frequency, which can improve the results of self-calibration with such models.

prodimopy is an open-source Python package to read, analyze and plot modelling results of the radiation thermo-chemical disk code ProDiMo (PROtoplanetary DIsk MOdel, https://prodimo.iwf.oeaw.ac.at). It also includes tools to run ProDiMo in 1D slap model mode, to run simple ProDimo model grids and to interface ProDiMo with 1D and 2D disk codes (i.e. use input structure from hydrodynamic models).

prodimopy can also be used independently of ProDiMo (no ProDiMo installation is required) and hence is also useful to extract information from already available ProDiMo models (e.g. as input for other codes) or for model comparison.

The numerical code RoSSBi3D (Rotating Systems Simulation Code for Bi-fluids) is designed for protoplanetary discs study at 2D and 3D. It is a finite volume code which is second order in time, features self-gravity (2D), and uses an exact Riemann solver to account for discontinuities. This FORTRAN 90 code solves the fully compressible inviscid Euler, continuity and energy conservation equations in polar coordinates for an ideal gas orbiting a central object. Solid particles are treated as a pressureless fluid and interact with the gas through aerodynamic forces. The code works on high performance computers thanks to the MPI standard (CPU).

nemiss calculates neutrino emission from an astrophysical jet. nemiss works as part of the PLUTO-nemiss-rlos pipeline. PLUTO (ascl:1010.045) produces a hydrodynamical jet. Then, nemiss calculates beamed neutrino emission at each eligible cell along a given direction in space. Finally, rlos (ascl:1811.009) produces a synthetic neutrino image of the jet along the given direction, taking into consideration the finite nature of the speed of light.

FASMA delivers the atmospheric stellar parameters (effective temperature, surface gravity, metallicity, microturbulence, macroturbulence, and rotational velocity) based on the spectral synthesis technique. This technique relies on the comparison of synthetic spectra with observations to yield the best-fit parameters under a χ2 minimization process. FASMA also delivers chemical abundances of 13 elements. Written in Python, the code is wrapped around MOOG (ascl:1202.009) which calculates the synthetic spectra. FASMA includes two grids of models in MOOG readable format, Kurucz and marcs, that cover the parameter space for both dwarf and giant stars with metallicity limit of -5.0 dex.

pygwb analyzes laser interferometer data and designs a gravitational wave background (GWB) search pipeline. Its modular and flexible codebase is tailored to current ground-based interferometers such as LIGO Hanford, LIGO Livingston, and Virgo, but can be generalized to other configurations. It is based on GWpy (ascl:1912.016) and bilby (ascl:1901.011) for optimal integration with widely-used gravitational wave data analysis tools. pygwb also includes a set of scripts to analyze data and perform large-scale searches on a high-performance computing cluster efficiently.

CosmoLattice performs lattice simulations of field dynamics in an expanding universe. The code can simulate the dynamics of interacting scalar field theories, Abelian U(1) gauge theories, and non-Abelian SU(2) gauge theories, either in flat spacetime or an expanding FLRW background, including the case of self-consistent expansion sourced by the fields themselves. It can also compute gravitational waves sourced by U(1) Abelian Gauge fields. The CosmoLattice platform can implement any system of dynamical equations suitable for discretization on a lattice, as it introduces its own language describing fields and operations between them, and hence can implement new libraries to solve arbitrary field problems (related or not to cosmology).

PIPPIN (PDI pipeline for NACO data) reduces the polarimetric observations made with the VLT/NACO instrument. It applies the Polarimetric Differential Imaging (PDI) technique to distinguish the polarized, scattered light from the (largely) un-polarized, stellar light. As a result, circumstellar dust can be uncovered. PIPPIN appropriately handles various instrument configurations, including half-wave plate and de-rotator usage, Wollaston beam-splitter, and wiregrid observations. As part of the PDI reduction, PIPPIN performs various levels of corrections for instrumental polarization and crosstalk.

FPFS (Fourier Power Function Shaplets) is a fast, accurate shear estimator for the shear responses of galaxy shape, flux, and detection. Utilizing leading-order perturbations of shear (a vector perturbation) and image noise (a tensor perturbation), the code determines shear and noise responses for both measurements and detections. Unlike methods that distort each observed galaxy repeatedly, the software employs analytical shear responses of select basis functions, including Shapelets basis and peak basis. FPFS is efficient and can process approximately 1,000 galaxies within a single CPU second, and maintains a multiplicative shear estimation bias below 0.5% even amidst blending challenges.