The SPEDAS (Space Physics Environment Data Analysis Software) framework supports multi-mission data ingestion, analysis and visualization for the Space Physics community. It standardizes the retrieval of data from distributed repositories, the scientific processing with a powerful set of legacy routines, the quick visualization with full output control and the graph creation for use in papers and presentations. SPEDAS includes a GUI for ease of use by novice users, works on multiple platforms, and though based on IDL, can be used with or without an IDL license. The framework supports plugin modules for multiple projects such as THEMIS, MMS, and WIND, and provides interfaces for software modules developed by the individual teams of those missions. A Python implementation of the framework, PySPEDAS (ascl:2405.005), is also available.

nessai performs nested sampling for Bayesian Inference and incorporates normalizing flows. It is designed for applications where the Bayesian likelihood is computationally expensive. nessai uses PyTorch and also supports the use of bilby (ascl:1901.011).

raynest, written in Python, computes Bayesian evidences and probability distributions using parallel chains.

pyADfit models accretion discs around astrophysical objects. The code provides functions to calculate physical quantities related to accretion disks and perform parameter estimation using observational data. The accretion disc model is the alpha-disc model while the parameter estimation can be performed with Nessai (ascl:2405.002), Raynest (ascl:2405.003), or CPnest (ascl:2205.021).

pySPEDAS (Python-based Space Physics Environment Data Analysis Software) supports multi-mission, multi-instrument retrieval, analysis, and visualization of heliophysics time series data. A Python implementation of SPEDAS (ascl:2405.001), it supports most of the capabilities of SPEDAS; it can load heliophysics data sets from more than 30 space-based and ground-based missions, coordinate transforms, interpolation routines, and unit conversions, and provide interactive access to numerous data sets. pySPEDAS also creates multi-mission, multi-instrument figures, includes field and wave analysis tools, and performs magnetic field modeling, among other functions.

ICPertFLRW, a Cactus code (ascl:1102.013) thorn, provides as initial conditions an FLRW metric perturbed with the comoving curvature perturbation Rc in the synchronous comoving gauge. Rc is defined as a sum of sinusoidals (20 in each x, y, and z direction) whose amplitude, wavelength, and phase shift are all parameters in param.ccl. While the metric and extrinsic curvature only have first order scalar perturbations, the energy density is computed exactly in full from the Hamiltonian constraint, hence vector and tensor perturbations are initially present at higher order. These are then passed to the CT_Dust thorn to be evolved.

GauPro fits a Gaussian process regression model to a dataset. A Gaussian process (GP) is a commonly used model in computer simulation. It assumes that the distribution of any set of points is multivariate normal. A major benefit of GP models is that they provide uncertainty estimates along with their predictions.

i-SPin simulates 3-component Schrodinger systems with and without gravity and with and without self-interactions while obeying SO(3) symmetry. The code allows the user to input desired parameters, along with initial conditions for the Schrodinger fields. Its three function modules then perform the main (drift-kick-drift) steps of the algorithm, track the fractional changes in total mass and spin in the system, and then plot results. The default plots are mass and spin density projections along with total mass and spin fractional changes.

morphen performs image analysis, multi-Sersic image fitting decomposition, and radio interferometric self-calibration, thus measuring basic image morphology and photometry. The code provides a state-of-the-art Python-based image fitting implementation based on the Sersic function. Geared, though not exclusively, toward radio astronomy, morphen's tools involve pure python, but also are integrated with CASA (ascl:1107.013) in order to work with common casatasks as well as WSClean (ascl:1408.023).

riddler automates fitting of type Ia supernovae spectral time series. The code is comprised of a series of neural networks trained to emulate radiative transfer simulations from TARDIS (ascl:1402.018). Emulated spectra are then fit to observations using nested sampling implemented in UltraNest (ascl:1611.001) to estimate the posterior distributions of model parameters and evidences.

MMLPhoto-z is a cross-modal contrastive learning approach for estimating photo-z of quasars. This method employs adversarial training and contrastive loss functions to promote the mutual conversion between multi-band photometric data features (magnitude, color) and photometric image features, while extracting modality-invariant features.

DirectSHT performs direct spherical harmonic transforms for point sets on the sphere. Given a set of points, defined by arrays of theta and phi (in radians) and weights, it provides the spherical harmonic transform coefficients alm. JAX (ascl:2111.002) can be used to speed up the computation; the code will automatically fall back to numpy if JAX is not present. The code is much faster when run on GPUs. When they are available and JAX is installed, the code automatically distributes computation and memory across them.

fitramp fits a ramp to a series of nondestructive reads and detects and rejects jumps. The software performs likelihood-based jump detection for detectors read out up-the-ramp; it uses the entire set of reads to compute likelihoods. The code compares the χ² value of a fit with and without a jump for every possible jump location. fitramp can fit ramps with and without fitting the reset value (the pedestal), and fit and mask jumps within or between groups of reads. It can also compute the bias of ramp fitting.

LTdwarfIndices studies spectral indices to determine whether one or more brown dwarfs are photometric variable candidates. For a single brown dwarf, it analyzes a given set of indices and outputs the number of graphs the object appears in in the variable area, whether it is a variable or non-variable candidate, and, optionally, an index-index or histogram plot. Using another code module, LTdwarftIndices can also analyze a set of sample indices for many brown dwarfs.

EF-TIGRE (Effective Field Theory of Interacting dark energy with Gravitational REdshift) constrains interacting Dark Energy/Dark Matter models in the Effective Field Theory framework through Large Scale Structures observables. In particular, the observables include the effect of gravitational redshift, a distortion of time from galaxy clustering. This generates a dipole in the correlation function which is detectable with two distinct populations of galaxies, thus making it possible to break degeneracies among parameters of the EFT description.

sunbather simulates the upper atmospheres of exoplanets and their observational signatures. The code constructs 1D Parker wind profiles using p-winds (ascl:2111.011) to simulate these with Cloudy (ascl:9910.001), and postprocesses the output with a custom radiative transfer module to predict the transmission spectra of exoplanets.

autoregressive-bbh-inference, written in Python, models the distributions of binary black hole masses, spins, and redshifts to identify physical features appearing in these distributions without the need for strongly-parametrized population models. This allows not only agnostic study of the “known unknowns” of the black hole population but also reveals the “unknown unknowns," the unexpected and impactful features that may otherwise be missed by the standard building-block method.

AFINO (Automated Flare Inference of Oscillations) finds oscillations in time series data using a Fourier-based model comparison approach. The code analyzes the date and generates a results file in either JSON or Pickle format, which contains numerous properties of the data and analysis, and a summary plot.

coronagraph_noise simulates coronagraph noise. Written in IDL, the code includes a generalized coronagraph routine and simulators for the WFIRST Shaped Pupil Coronagraph in both spectroscopy and imaging modes. Functions available include stellar and planetary flux functions, planet photon and zodiacal light count rates, planet-star flux ratio, and clock induced charge count rate, among others. coronagraph_noise also includes routines to smooth a plot by convolving with a Gaussian profile to convolve a spectrum with a given instrument resolution and to take a spectrum that is specified at high spectral resolution and degrade it to a lower resolution. A Python implementation of coronagraph_noise, coronagraph (ascl:2405.019), is also available.

coronagraph provides a Python noise model for directly imaging exoplanets with a coronagraph-equipped telescope. Based on the original IDL code for this coronagraph model, coronograph_noise (ascl:2405.018), the Python version has been expanded in a few key ways. Most notably, the Telescope, Planet, and Star objects used for reflected light coronagraph noise modeling can now be used for transmission and emission spectroscopy noise modeling, making this model a general purpose exoplanet noise model for many different types of observations.

tapify implements a suite of multitaper spectral estimation techniques for analyzing time series data. It supports analysis of both evenly and unevenly sampled time series data. The multitaper statistic tackles the problems of bias and consistency, which makes it an improvement over the classical periodogram for evenly sampled data and the Lomb-Scargle periodogram for uneven sampling. In basic statistical terms, this estimator provides a confident look at the properties of a time series in the frequency or Fourier domain.

PALpy provides a Python interface to PAL, the positional Astronomy Library (ascl:1606.002), which is written in C. All arguments modified by the C API are returned and none are modified. The one routine that is different is palObs, which returns a simple dict that can be searched using standard Python. The keys to the dict are the short names and the values are another dict with keys name, long, lat and height.

blackthorn generates spectra of dark matter annihilations into right-handed (RH) neutrinos or into particles that result from their decay. These spectra include photons, positrons, and neutrinos. The code provides support for varied RH-neutrino masses ranging from MeV to TeV by incorporating hazma, PPPC4DMID, and HDMSpectra models to compute dark matter annihilation cross sections and mediator decay widths. blackthorn also computes decay branching fractions and partial decay widths.

raccoon implements the cross-correlation function (CCF) method. It builds weighted binary masks from a stellar spectrum template, computes the CCF of stellar spectra with a mask, and derives radial velocities (RVs) and activity indicators from the CCF. raccoon is mainly implemented in Python 3; it also uses some Fortran subroutines that are called from Python.

ndcube manipulates, inspects, and visualizes multi-dimensional contiguous and non-contiguous coordinate-aware data arrays. A sunpy (ascl:1401.010) affiliated package, it combines data, uncertainties, units, metadata, masking, and coordinate transformations into classes with unified slicing and generic coordinate transformations and plotting and animation capabilities. ndcube handles data of any number of dimensions and axis types (e.g., spatial, temporal, and spectral) whose relationship between the array elements and the real world can be described by World Coordinate System (WCS) translations.

CosmoPower develops Bayesian inference pipelines that leverage machine learning to solve inverse problems in science. While the emphasis is on building algorithms to accelerate Bayesian inference in cosmology, the implemented methods allow for their application across a wide range of scientific fields. CosmoPower provides neural network emulators of matter and Cosmic Microwave Background power spectra, which can replace Boltzmann codes such as CAMB (ascl:1102.026) or CLASS (ascl:1106.020) in cosmological inference pipelines, to source the power spectra needed for two-point statistics analyses. This provides orders-of-magnitude acceleration to the inference pipeline and integrates naturally with efficient techniques for sampling very high-dimensional parameter spaces.

pycosie is analysis code used for Technicolor Dawn (TD), a Gadget-3 derived cosmological radiative SPH simulation suite. The target analyses are to complement what is done with TD and other analysis software on its suite. pycosie includes creating power spectrum from generated Lyman-alpha forests spectra, linking absorbers to potential host galaxies, gridding gas information for each galaxy, and read specific output files from software such as Rockstar and SKID.

GAStimator implements a Python MCMC Gibbs-sampler with adaptive stepping. The code is simple, robust, and stable and well suited to high dimensional problems with many degrees of freedom and very sharp likelihood features. It has been used extensively for kinematic modeling of molecular gas in galaxies, but is fully general and may be used for any problem MCMC methods can tackle.

Scaling Relations Finder finds the scaling relations between magnetic field properties and observables for a model of galactic magnetic fields. It uses observable quantities as input: the galaxy rotation curve, the surface densities of the gas, stars and star formation rate, and the gas temperature to create galactic dynamo models. These models can be used to estimate parameters of the random and mean components of the magnetic field, as well as the gas scale height, root-mean-square velocity and the correlation length and time of the interstellar turbulence, in terms of the observables.

SMART (Spectral energy distributions Markov chain Analysis with Radiative Transfer models) implements a Bayesian Markov chain Monte Carlo (MCMC) method to fit the ultraviolet to millimeter spectral energy distributions (SEDs) of galaxies exclusively with radiative transfer models. The models constitute four types of pre-computed libraries, which describe the starburst, active galactic nucleus (AGN) torus, host galaxy and polar dust components.

candl (CMB Analysis With A Differentiable Likelihood) analyzes CMB power spectrum measurements using a differentiable likelihood framework. It is compatible with JAX (ascl:2111.002), though JAX is optional, allowing for fast and easy computation of gradients and Hessians of the likelihoods, and candl provides interface tools for working with other cosmology software packages, including Cobaya (ascl:1910.019) and MontePython (ascl:1805.027). The package also provides auxiliary tools for common analysis tasks, such as generating mock data, and supports the analysis of primary CMB and lensing power spectrum data.

Lenser estimates weak gravitational lensing signals, particularly flexion, from real survey data or realistically simulated images. Lenser employs a hybrid of image moment analysis and an Analytic Image Modeling (AIM) analysis. In addition to extracting flexion measurements by fitting a (modified Sérsic) model to a single image of a galaxy, Lenser can do multi-band, multi-epoch fitting. In multi-band mode, Lenser fits a single model to multiple postage stamps, each representing an exposure of a single galaxy in a particular band.

The anzu package offers two independent codes for hybrid Lagrangian bias models in large-scale structure. The first code measures the hybrid "basis functions"; the second takes measurements of these basis functions and constructs an emulator to obtain predictions from them at any cosmology (within the bounds of the training set). anzu is self-contained; given a set of N-body simulations used to build emulators, it measures the basis functions. Alternatively, given measurements of the basis functions, anzu should in principle be useful for constructing a custom emulator.

CARDiAC (Code for Anisotropic Redshift Distributions in Angular Clustering) computes the impact of anisotropic redshift distributions on a wide class of angular clustering observables. It supports auto- and cross-correlations of galaxy samples and cosmic shear maps, including galaxy-galaxy lensing. The anisotropy can be present in the mean redshift and/or width of Gaussian distributions, as well as in the fraction of galaxies in each component of multi-modal distributions. Templates of these variations can be provided by the user or simulated internally within the code.

sphereint calculates the numerical volume in a sphere. It provides a weight for each grid position based on whether or not it is in (weight = 1), out (weight = 0), or partially in (weight in between 0 and 1) a sphere of a given radius. A cubic cell is placed around each grid position and the volume of the cell in the sphere (assuming a flat surface in the cell) is calculated and normalized by the cell volume to obtain the weight.

CBiRd (Code for Bias tracers In Redshift space) provides correlators in the Effective Field Theory of Large-Scale Structure (EFTofLSS) in a ready-to-use pipeline for cosmological analysis of galaxy-redshift surveys data. It provides a core calculation package (C++BiRd), a Python implementation of a Taylor expansion of the power spectrum around a reference cosmology for efficient evaluation (TBiRd), and libraries to correct for observational systematics. CBiRd also provides MCMC samplers (MCBiRd) for a power spectrum and bispectrum analysis of galaxy-redshift surveys data based on emcee (ascl:1303.002), and can provide an earlybird pass to explore the cosmos with LSS surveys.

PRyMordial offers fast and precise evaluation of both the Big Bang Nucleosynthesis (BBN) light-element abundances and the effective number of relativistic degrees of freedom. It can be used within and beyond the Standard Model. The package calculates N_eff and helium-4, deuterium, helium-3 and lithium-7 abundances. PRyMordial corrects for QED plasma effects, neutron lifetime, and incomplete neutrino decoupling, and includes an optional module that re-elaborates all the ODE systems of the code in Julia.

Color transformations calculator determines the magnitude of a galaxy in a needed photometric band, given its color and magnitude in the original band. It supports various optical and near intrared surveys, including SDSS, DECaLS, DELVE, UKIDSS, VHS, and VIKING, and provides conversions for both total and aperture magnitudes with apertures of 1.5", 2" or 3" diameters. The source code, useful for performing bulk calculations, is available in Python and IDL; the calculator is also offered as a web service.

Quadratic Monte Carlo generates ensembles of models and confines fitness landscapes without relying on linear stretch moves; it works very efficiently for ring potential and Rosenbrock density. The method is general and can be implemented into any existing MC software, requiring only a few lines of code.

The ALeRCE anomaly detector cross-validates six anomaly detection algorithms for three classes (transient, periodic, and stochastic) of anomalous sources within the Zwicky Transient Facility (ZTF) data stream using the ALeRCE light curve features. A machine and deep learning-based framework is used for anomaly detection. For each class, a distinct anomaly detection model is constructed using only information about the known objects (i.e., inliers) for training. An anomaly score is computed using the probabilities to determine whether the light curve corresponds to a transient, stochastic, or periodic nature.

EVA (Excess Variability-based Age) computes the VarX values and VarX90 ages for a given list of stars. The package retrieves information from Gaia, performs basic var90 calculations, then calculates the age of the group in a given band or overall (by combining all three bands). EVA then analyzes and plots the results.

FLORAH generates the assembly history of halos using a recurrent neural network and normalizing flow model. The machine-learning framework can be used to combine multiple generated networks that are trained on a suite of simulations with different redshift ranges and mass resolutions. Depending on the training, the code recovers key properties, including the time evolution of mass and concentration, and galaxy stellar mass versus halo mass relation and its residuals. FLORAH also reproduces the dependence of clustering on properties other than mass, and is a step towards a machine learning-based framework for planting full merger trees.

BiaPy provides deep-learning workflows for a large variety of image analysis tasks, including 2D and 3D semantic segmentation, instance segmentation, object detection, image denoising, single image super-resolution, self-supervised learning and image classification. Though developed specifically for bioimages, it can be used for watershed-based instance segmentation for friends-of-friends proto-haloes.

ytree reads and works with merger tree data from multiple formats. An extension of yt (ascl:1011.022), which can analyze snapshots from cosmological simulations, ytree can be thought of as the yt of merger trees. ytree's online documentation lists supported formats; support for additional formats can be added, as in principle, any type of tree-like data where an object has one or more ancestors and a single descendant can be supported.

SuperLite produces synthetic spectra for astrophysical transient phenomena affected by circumstellar interaction. It uses Monte Carlo methods and multigroup structured opacity calculations for semi-implicit, semirelativistic radiation transport in high-velocity shocked outflows, and can reproduce spectra of typical Type Ia, Type IIP, and Type IIn supernovae. SuperLite also generates high-quality spectra that can be compared with observations of transient events, including superluminous supernovae, pulsational pair-instability supernovae, and other peculiar transients.

Magnification bias estimation estimates magnification bias for a galaxy sample with a complex photometric selection for the example of SDSS BOSS. The code works for CMASS and the LOWZ, z1 and z3 samples. A template for applying the approach to other surveys is included; requirements include a galaxy catalog that provides magnitudes (used for photometric selection) and the exact conditions used for the photometric selection.

LeHaMoC simulates high-energy astrophysical sources. It simulates the behavior of relativistic pairs, protons interacting with magnetic fields, and photons in a spherical region. The package contains numerous physical processes, including synchrotron emission and self-absorption, inverse Compton scattering, photon-photon pair production, and adiabatic losses. It also includes proton-photon pion production, proton-photon (Bethe-Heitler) pair production, and proton-proton collisions. LeHaMoC can model expanding spherical sources with a variable magnetic field strength. In addition, three types of external radiation fields can be defined: grey body or black body, power-law, and tabulated.

Photochem models the photochemical and climate composition of a planet's atmosphere. It takes inputs such as the stellar UV flux and atmospheric temperature structure to find the steady-state chemical composition of an atmosphere, or evolve atmospheres through time. Photochem also contains 1-D climate models and a chemical equilibrium solver.

Phazap post-processes gravitational-wave (GW) parameter estimation data to obtain the phases and polarization state of the signal at a given detector and frequency. It is used for low-latency identification of strongly lensed gravitational waves via their phase consistency by measuring their distance in the detector phase space. Phazap builds on top of the IGWN conda enviroment which includes the standard GW packages LALSuite (ascl:2012.021) and bilby (ascl:1901.011), and can be applied beyond lensing to test possible deviations in the phase evolution from modified theories of gravity and constrain GW birefringence.

This python code automatically detects solar active regions (AR). Based on morphological operation and region growing, it uses synoptic magnetograms from SOHO/MDI and SDO/HMI and calculates the parameters that characterize each AR, including the latitude and longitude of the flux-weighted centroid of two polarities and the whole AR, the area, and the flux of each polarity, and the initial and final dipole moments.

GRINN (Gravity Informed Neural Network) solves the coupled set of time-dependent partial differential equations describing the evolution of self-gravitating flows in one, two, and three spatial dimensions. It is based on physics informed neural networks (PINNs), which are mesh-free and offer a fundamentally different approach to solving such partial differential equations. GRINN has solved for the evolution of self-gravitating, small-amplitude perturbations and long-wavelength perturbations and, when modeling 3D astrophysical flows, provides accuracy on par with finite difference (FD) codes with an improvement in computational speed.

PowerSpecCovFFT compute the non-Gaussian (regular trispectrum and its shot noise) part of the analytic covariance matrix of the redshift-space galaxy power spectrum multipoles using an FFTLog-based method. The galaxy trispectrum is based on a tree-level standard perturbation theory but with a slightly different galaxy bias expansion. The code computes the non-Gaussian covariance of the power spectrum monopole, quadrupole, hexadecapole, and their cross-covariance up to kmax ~ 0.4 h/Mpc.

Faceted-HyperSARA images radio-interferometric wideband intensity data. Written in MATLAB, the library offers a collection of utility functions and scripts from data extraction from an RI measurement set MS Table to the reconstruction of a wideband intensity image over the field of view and frequency range of interest. The code achieves high precision imaging from large data volumes and supports data dimensionality reduction via visibility gridding and estimation of the effective noise level when reliable noise estimates are not available. Faceted-HyperSASA also corrects the w-term via w-projection and incorporates available compact Fourier models of the direction dependent effects (DDEs) in the measurement operator.

The phi-GPU (Parallel Hermite Integration on GPU) high-order N-body parallel dynamic code uses the fourth-order Hermite integration scheme with hierarchical individual block time-steps and incorporates external gravity. The software works directly with GPU, using only NVIDIA GPU and CUDA code. It creates numerical simulations and can be used to study galaxy and star cluster evolution.

Redback provides end-to-end interpretation and parameter estimation of electromagnetic transients. Using data downloaded by the code or provided by the user, the code processes the data into a homogeneous transient object. Redback implements several different types of electromagnetic transients models, ranging from simple analytical models to numerical surrogates, fits models implemented in the package or provided by the user, and plots lightcurves. The code can also be used as a tool to simulate realistic populations without having to fit anything, as models are implemented as functions and can be used to simulate populations. Redback uses Bilby (ascl:1901.011) for sampling and can easily switch samplers and likelihoods.

AutoPhOT (AUTOmated Photometry Of Transients) produces publication-quality photometry of transients quickly. Written in Python 3, this automated pipeline's capabilities include aperture and PSF-fitting photometry, template subtraction, and calculation of limiting magnitudes through artificial source injection. AutoPhOT is also capable of calibrating photometry against either survey catalogs (e.g., SDSS, PanSTARRS) or using a custom set of local photometric standards.