Results 751-800 of 3595 (3502 ASCL, 93 submitted)

[ascl:2205.012]
Zelda: Generate correlation functions and power spectra from a galaxy catalog

The Zelda command-line tool extracts correlation functions in velocity space from a galaxy catalog. Zelda is modular, extendable, and can be generalized to produce power spectra and to work in position space. Written in C, it was heavily inspired by the cosmological Boltzmann code CLASS (ascl:1106.020). Zelda is a parallel code using the OpenMP standard.

[ascl:2205.011]
myRadex: Radex with a twist

myRadex solves essentially the same problem as RADEX (ascl:1010.075), except that it takes a different approach to solve the statistical equilibrium problem. Given an initial distribution, myRadex evolves the system towards equilibrium using an ODE solver. Frequencies in the input file are used by default, and a function for calculating critical densities of all the transitions of a molecule is included.

[ascl:2205.010]
pyICs: Initial Conditions creator for isolated galaxy formation simulations

pyICs creates initial condition (IC) files for N-body simulations of the formation of isolated galaxies. It uses the pynbody analysis package (ascl:1305.002) to create the actual IC files. pyICs generates dark matter halos (DM) in dynamical equilibrium which host a rotating gas sphere. The DM particle velocities are drawn from the equilibrium distribution function and the gas sphere has an angular momentum profile. The DM and the gas share the same 3D radial density profile. The code natively supports the αβγ-models: ρ ~ (r/a)-γ[1+(r/a)α](γ-β)/α. If γ <= 3, the profiles are smoothly truncated outside the virial radius. The radial profile can be arbitrary as long as python functions for the profile itself and its first and second derivative with radius are given.

[ascl:2205.009]
hyperas: Keras + Hyperopt

Hyperas is a convenience wrapper around hyperopt (ascl:2205.008) for fast prototyping with keras models (ascl:1806.022). Hyperas lets you use the power of hyperopt without having to learn the syntax of it. Instead, just define your keras model as you are used to, but use a simple template notation to define hyper-parameter ranges to tune.

[ascl:2205.008]
Hyperopt: Distributed asynchronous hyper-parameter optimization

The Python library Hyperopt performs serial and parallel optimization over awkward search spaces, which may include real-valued, discrete, and conditional dimensions. Three algorithms are implemented in hyperopt: Random Search, Tree of Parzen Estimators (TPE), and Adaptive TPE. Algorithms can be parallelized in two ways, using either Apache Spark or MongoDB. To use Hyperopt, the objective function to minimize and the space over which to search, and the database in which to store all the point evaluations of the search have to be described, and the search algorithm to use has to be specified.

[ascl:2205.007]
EarthScatterLikelihood: Event rates and likelihoods for Dark Matter direct detection in the presence of Earth-Scattering

EarthScatterLikelihood calculates event rates and likelihoods for Earth-scattering Dark Matter. It is written in Fortran with plotting routines in Python. For input, it uses results from Monte Carlo simulations generated by DaMaSCUS (ascl:1706.003). It includes routines for submitting many reconstructions in parallel on a cluster, and the properties of the detector, such as for a Germanium and a Sapphire detector, can be edited.

[ascl:2205.006]
LATTE: Lightcurve Analysis Tool for Transiting Exoplanet

LATTE identifies, vets and characterizes signals in TESS lightcurves to weed out instrumental and astrophysical false positives. The program performs a fast in-depth analysis of targets that have already been identified as promising candidates by the main TESS pipelines or via alternative methods such as citizen science. The code automatically downloads the data products for any chosen TIC ID (short or long cadence TESS data) and produces a number of diagnostic plots that are compiled in a concise report.

[ascl:2205.005]
maelstrom: Forward modeling of pulsating stars in binaries

Hey, Daniel R.; Murphy, Simon J.; Foreman-Mackey, Dan; Bedding, Timothy R.; Pope, Benjamin J.S.; Hogg, David W.

maelstrom models binary orbits through the phase modulation technique. This set of custom PyMC3 models and solvers fit each individual datapoint in the time series by forward modeling the time delay onto the light curve. This approach fully captures variations in a light curve caused by an orbital companion.

[ascl:2205.004]
FAlCon-DNS: Framework of time schemes for direct numerical simulation of annular convection

FAlCon-DNS (Framework of time schemes for direct numerical simulation of annular convection) solves for 2-D convection in an annulus and analyzes different time integration schemes. The framework contains a suite of IMEX, IMEXRK and RK time integration schemes. The code uses a pseudospectral method for spatial discretization. The governing equations contain both numerically stiff (diffusive) and non-stiff (advective) components for time discretization. The software offers OpenMP for parallelization.

[ascl:2205.003]
QSOGEN: Model quasar SEDs

The QSOGEN collection of Python code models quasar colors, magnitudes and SEDs. It implements an empirically-motivated parametric model to efficiently account for the observed emission-line properties, host-galaxy contribution, dust reddening, hot dust emission, and IGM suppression in the rest-frame 900-30000A wavelength range for quasars with a wide range of redshift and luminosity.

The code is packaged with a set of empirically-derived emission-line templates and an empirically-derived quasar dust extinction curve which are publicly released.

[ascl:2205.002]
am: Microwave through submillimeter-wave propagation tool for the terrestrial atmosphere

am performs optical depth, radiative transfer, and refraction computations involving propagation through the terrestrial atmosphere and other media at microwave through submillimeter wavelengths. The program is used in radio astronomy, atmospheric radiometry, and radio spectrum management.

[ascl:2205.001]
PMOIRED: Parametric Modeling of Optical Interferometric Data

PMOIRED models astronomical spectro-interferometric data stored in the OIFITS format. Parametric modeling is used to describe the observed scene as blocks such as disks, rings and Gaussians which can be combined and their parameters linked. It includes plotting, least-square fitting and bootstrapping estimation of uncertainties. For spectroscopic instruments (such as GRAVITY), tools are provided to model spectral lines and correct spectra for telluric lines.

[submitted]
Python “sgp4” module that offers official SGP4 C++ library

The “sgp4” module is a Python wrapper around the C++ version of the standard SGP4 algorithm for propagating Earth satellite positions from the element sets published by organizations like SpaceTrak and Celestrak. The code is the most recent version, including all of the corrections and bug fixes described in the paper _Revisiting Spacetrack Report #3_ (AIAA 2006-6753) by Vallado, Crawford, Hujsak, and Kelso. The test suite verifies that the Python wrapper returns exactly the coordinates specified in the C++ test cases.

[submitted]
SWIFTGalaxy

SWIFTGalaxy provides a software abstraction of simulated galaxies produced by the SWIFT smoothed particle hydrodynamics code. It extends the SWIFTSimIO module and is tailored to analyses of particles belonging to individual simulated galaxies. It inherits from and extends the functionality of the SWIFTDataset. It understands the output of halo finders and therefore which particles belong to a galaxy, and its integrated properties. The particles occupy a coordinate frame that is enforced to be consistent, such that particles loaded on-the-fly will match e.g. rotations and translations of particles already in memory. Intuitive masking of particle datasets is also enabled. Finally, some utilities to make working in cylindrical and spherical coordinate systems more convenient are also provided.

[ascl:2204.020]
MonoTools: Planets of uncertain periods detector and modeler

MonoTools detects, vets, and models transiting exoplanets, with a specific emphasis on monotransiting planets and those with unknown periods. It includes scripts specifically for searching and assessing a lightcurve for the presence of monotransits. MonoTools can also performing a best-fit transit model, determine whether transits are linked to any detected multi-transiting planet candidate or with each other, and can fit planets in a Bayesian way to account for uncertain periods, lightcurve gaps, and stellar variability, among other things.

[ascl:2204.019]
DarkFlux: Dark Matter annihilation spectrum computer

DarkFlux analyzes indirect-detection signatures for next-generation models of dark matter (DM) with multiple annihilation channels. Input is user-generated models with 2 → 2 tree-level dark matter annihilation to pairs of Standard Model (SM) particles. The code analyzes DM annihilation to γ rays using three modules; one computes the fractional annihilation rate, the second computes the total flux at Earth due to DM annihilation, and the third compares the total flux to observational data and computes the upper limit at 95% confidence level (CL) on the total thermally averaged DM annihilation cross section.

[ascl:2204.018]
ProFuse: Galaxies and components modeler

ProFuse produces physical models of galaxies and their components by combining the functionalities of the source extraction code PROFOUND (ascl:1804.006), the Bayesian galaxy fitting tool ProFit (ascl:1612.004), and the spectral generation package ProSpect (ascl:2002.007). ProFuse uses a self-consistent model for the star formation and metallicity history of the bulge and disk separately to generate images. The package then defines the model likelihood and optimizes the physical galaxy reconstruction using target images across a range of wavelengths.

[ascl:2204.017]
RSG: Redshift Search Graphs

Redshift Search Graphs provides a fast and reliable way to test redshifts found from sub-mm redshift searches. The scripts can graphically test the robustness of a spectroscopic redshift of a galaxy, test the efficiency of an instrument towards spectroscopic redshift searches, and optimize observations of tunable institutes (such as ALMA) for upcoming redshift searches.

[ascl:2204.016]
pySIDES: Simulated Infrared Dusty Extragalactic Sky in Python

pySIDES generates mock catalogs of galaxies in the (sub-)millimeter domain and associates spectral cubes (e.g., for intensity mapping experiments). It produces both continuum and CO, [CII], and [CI] line emissions. pySIDES is the Python version of the Simulated Infrared Dusty Extragalactic Sky (SIDES).

[ascl:2204.015]
ADBSat: Aerodynamic Database for Satellites

Sinpetru, Luciana A.; Crisp, Nicholas H.; Roberts, Peter C. E.; Sulliotti-Linner, Valeria; Hanessian, Virginia; Herdrich, Georg H.; Romano, Francesco; Garcia-Almiñana, Daniel; Rodríguez-Donaire, Sílvia; Seminari, Simon; Mostaza-Prieto, David; Livadiotti, Sabrina

ADBSat computes aerodynamic coefficient databases for satellite geometries in free-molecular flow (FMF) conditions. Written in MATLAB, ADBSat imports body geometry from .stl or .obj mesh files, calculates aerodynamic force and moment coefficient for different gas-surface interaction models, and calculates solar radiation pressure force and moment coefficient. It also takes multiple surface and material characteristics into consideration. ADBSat is a panel-method tool that is able to calculate aerodynamic or solar force and moment coefficient sets for satellite geometries by applying analytical (closed-form) expressions for the interactions to discrete flat-plate mesh elements. The panel method of ADBSat assumes FMF conditions. The code analyzes basic shadowing to identify panels that are shielded from the flow by other parts of the body and will therefore not experience any surface interactions. However, this method is dependent on the refinement of the input mesh and can be sensitive to the orientation and arrangement of the mesh elements with respect to the oncoming flow direction.

[ascl:2204.014]
GADGET-4: Parallel cosmological N-body and SPH code

GADGET-4 (GAlaxies with Dark matter and Gas intEracT) is a parallel cosmological N-body and SPH code that simulates cosmic structure formation and calculations relevant for galaxy evolution and galactic dynamics. It is massively parallel and flexible, and can be applied to a variety of different types of simulations, offering a number of sophisticated simulation algorithms. GADGET-4 supports collisionless simulations and smoothed particle hydrodynamics on massively parallel computers.

The code can be used for plain Newtonian dynamics, or for cosmological integrations in arbitrary cosmologies, both with or without periodic boundary conditions. Stretched periodic boxes, and special cases such as simulations with two periodic dimensions and one non-periodic dimension are supported as well. The modeling of hydrodynamics is optional. The code is adaptive both in space and in time, and its Lagrangian character makes it particularly suitable for simulations of cosmic structure formation. Several post-processing options such as group- and substructure finding, or power spectrum estimation are built in and can be carried out on the fly or applied to existing snapshots. Through a built-in cosmological initial conditions generator, it is also particularly easy to carry out cosmological simulations. In addition, merger trees can be determined directly by the code.

[ascl:2204.013]
SCRIPT: Semi-numerical Code for ReIonization with PhoTon-conservation

SCRIPT (Semi-numerical Code for ReIonization with PhoTon-conservation) generates the ionization field during the epoch of cosmological reionization using a photon-conserving algorithm. The code depends on density and velocity files obtained using a N-body simulation, which can be generated with a 2LPT code such as MUSIC (ascl:1311.011).

[ascl:2204.012]
SpECTRE: Multi-scale, multi-physics problem solver

Deppe, Nils; Throwe, William; Kidder, Lawrence E.; Vu, Nils L.; Hébert, François; Moxon, Jordan; Armaza, Cristóbal; Bonilla, Gabriel S.; Kim, Yoonsoo; Kumar, Prayush; Lovelace, Geoffrey; Macedo, Alexandra; Nelli, Kyle C.; O'Shea, Eamonn; Pfeiffer, Harald P.; Scheel, Mark A.; Teukolsky, Saul A.; Wittek, Nikolas A.; Anantpurkar, Isha; Boyle, Michael; Carpenter, Alexander; Chaudhary, Himanshu; Foucart, Francois; Ghadiri, Noora; Giesler, Matthew; Guo, Jason S.; Iozzo, Dante A. B.; Legred, Isaac; Li, Dongjun; Ma, Sizheng; Melchor, Denyz; Morales, Marlo; Ramirez, Teresita; Rüter, Hannes R.; Sanchez, Jennifer; Thomas, Sierra; Vieira, Daniel; Wlodarczyk, Tom

SpECTRE solves multi-scale, multi-physics problems in astrophysics and gravitational physics, such as those associated with the multi-messenger astrophysics of neutron star mergers, core-collapse supernovae, and gamma-ray bursts. It runs at petascale and is designed for future exascale computers.

[ascl:2204.011]
SimAb: Planet formation model

SimAb (Simulating Abundances) simulates planet formation, focusing on the atmosphere accretion of gas giant planets. The package can run the simulation in two different modes. The single simulation mode is run by specifying the initial conditions (the core mass, the initial orbital distance, the planetesimal ratio, and the dust grain fraction), and the mature planet mass and orbital distance. The multi run simulation mode requires specifying the mass and the final orbital distance of the mature planet; the simulation randomly assigns initial orbital distance, initial core mass, initial planetesimal ratio, and initial dust grain fraction. The package also provides Jupyter codes for plotting the results of the simulations.

[ascl:2204.010]
FBCTrack: Fragmentation and bulk composition tracking

The fragmentation and bulk composition tracking package contains two codes. The fragmentation code models fragmentation in collisions for the C version of REBOUND (ascl:1110.016). This code requires setting two global parameters. It automatically produces a collision report that details the time of every collision, the bodies involved, how the collision was resolved, and how many fragments were produced; collision outcomes are assigned a numerical value. The bulk composition tracking code tracks the composition change as a function of mass exchange for bodies with a homogenous composition. It is a post-processing code that works in conjunction with the fragmentation code, and requires the collision report generated by the fragmentation code.

[ascl:2204.009]
MAYONNAISE: ADI data imaging processing pipeline

MAYONNAISE (Morphological Analysis Yielding separated Objects iN Near infrAred usIng Sources Estimation), or MAYO for short, is a pipeline for exoplanet and disk high-contrast imaging from ADI datasets. The pipeline is mostly automated; the package also loads the data and injects synthetic data if needed. MAYONNAISE parameters are written in a json file called parameters_algo.json and placed in a working_directory.

[ascl:2204.008]
RMNest: Bayesian approach to measuring Faraday rotation and conversion in radio signals

RMNest directly fits the Stokes Q and U (and V) spectra of a radio signal to measure the effects of Faraday rotation (or conversion) induced by propagation through a magnetized plasma along the line of sight. The software makes use of the Bayesian Inference Library (Bilby; ascl:1901.011) as an interface to the dynesty (ascl:1809.013) nested sampling algorithm.

[ascl:2204.007]
RTS: Radio Transient Simulations

Radio Transient Simulations uses Monte-Carlo simulations to accurately determine transient rates in radio surveys. The user inputs either a real or simulated observational setup, and the simulations code calculates transient rate as a function of transient duration and peak flux. These simulations allow for simulating a wide variety of scenarios including observations with varying sensitivities and durations, multiple overlapping telescope pointings, and a wide variety of light curve shapes with the user having the ability to easily add more. Though the current scientific focus is on the radio regime, the simulations code can be easily adapted to other wavelength regimes.

[ascl:2204.006]
dsigma: Galaxy-galaxy lensing Python package

dsigma analyzes galaxy-galaxy lensing. Written in Python, it has a broadly applicable API and is optimized for computational efficiency. While originally intended to be used with the shape catalog of the Hyper-Suprime Cam (HSC) survey, it should work for other surveys, most prominently the Dark Energy Survey (DES) and the Kilo-Degree Survey (KiDS).

[ascl:2204.005]
TESS-Localize: Localize variable star signatures in TESS Photometry

TESS-Localize identifies the location on the target pixel files (TPF) where sources of variability found in the aperture originate. The user needs only to provide a list of frequencies found in the aperture that belong to the same source and the number of principal components needed to be removed from the light curve to ensure it is free of systematic trends.

[ascl:2204.004]
Bayesian SZNet: Bayesian deep learning to predict redshift with uncertainty

Bayesian SZNet predicts spectroscopic redshift through use of a Bayesian convolutional network. It uses Monte Carlo dropout to associate predictions with predictive uncertainties, allowing the user to determine unusual or problematic spectra for visual inspection and thresholding to balance between the number of incorrect redshift predictions and coverage.

[ascl:2204.003]
legacystamps: Retrieve DESI Legacy Imaging Surveys cutouts

The Python module legacystamps provides easy retrieval, both standalone and scripted, of FITS and JPEG cutouts from the DESI Legacy Imaging Surveys through URLs provided by the Legacy Survey viewer.

[ascl:2204.002]
Astroplotlib: Python scripts to handle astronomical images

Astroplotlib builds images with any scale, overlay contours, physical bars, and orientation arrows (N and E axes) automatically. The package contains scripts to overlay pseudo-slits and obtain statistics from apertures, estimate the background sky, and overlay the fitted isophotes and their respective contours on an image. Astroplotlib can work with the output table from the Ellipse task of IRAF and overlay fitted isophotes and their respective contours. It includes a GUI for masking areas in the images by using different polygons, and can also obtain statistical information (*e.g.*, total flux and mean, among others) from the masked areas. There is also a GUI to overlay star catalogs on an image and an option to download them directly from the Vizier server.

[ascl:2204.001]
TG: Turbulence Generator

Turbulence Generator generates a time sequence of random Fourier modes via an Ornstein-Uhlenbeck (OU) process, used to drive turbulence in hydrodynamical simulation codes. It can also generate single turbulent realizations. Turbulence driving based on this method is currently supported by implementations in AREPO (ascl:1909.010), FLASH (ascl:1010.082), GADGET (ascl:0003.001), PHANTOM (ascl:1709.002), PLUTO (ascl:1010.045), and Quokka (ascl:2110.009).

[ascl:2203.031]
TAWAS: Wave equation solver

TAWAS solves the wave equation for torsional Alfvèn waves in a viscous plasma. The background magnetic field is axisymmetric and force-free with no azimuthal component and the plasma beta is assumed to be negligible. The solution is calculated over a uniform numerical grid with coordinates r and z for the radius and height respectively. TAWAS, written in IDL, requires no input files. The problem parameters at the top of the code can be changed as need. The 'plotting' variable determines which plots are shown by the script; the code contains several options for plotting. Outputs can be saved to a specific location by changing the variables save_dir and run_name listed just below the parameters. The code outputs include solutions for the velocity perturbation, the magnetic field perturbation and the wave energy flux.

[ascl:2203.030]
Wigglewave: Linearized governing equations solver

Wigglewave uses a finite difference method to solve the linearized governing equations for a torsion Alfvèn wave propagating in a plasma with negligible plasma beta and in a force-free axisymmetric magnetic field with no azimuthal component embedded in a high density divergent tube structure. Wigglewave is fourth order in time and space using a fourth-order central difference scheme for calculating spatial derivatives and a fourth-order Runge-Kutta (RK4) scheme for updating at each timestep. The solutions calculated are the perturbations to the velocity, v and to the magnetic field, b. All variables are calculated over a uniform grid in radius r and height z.

[ascl:2203.029]
Bootsik: Potential field calculator

The Bootsik software generates and visualizes potential magnetic fields. bootsik.f90 generates a potential magnetic field on a 3D mesh, staggered relative to the magnetic potential, by extrapolating the magnetic field normal to the photospheric surface. The code first calculates a magnetic potential using a modified Green’s function method and then uses a finite differencing scheme to calculate the magnetic field from the potential. The IDL script boobox.pro can then be used to visualize the magnetic field.

[ascl:2203.028]
SimLine: Radiative transfer in molecular lines

SimLine computes the profiles of molecular rotational transitions and atomic fine structure lines in spherically symmetric clouds with arbitrary density, temperature and velocity structure. The code is designed towards a maximum flexibility and very high accuracy based on a completely adaptive discretization of all quantities. The code can treat arbitrary species in spherically symmetric configurations with arbitrary velocity structures and optical depths between about -5 and 5000. Moreover, SimLine includes the treatment of turbulence and clumping effects in a local statistical approximation combined with a radial dependence of the correlation parameters. The code consists of two parts: the self-consistent solution of the balance equations for all level populations and energy densities at all radial points and the computation of the emergent line profiles observed from a telescope with finite beam width and arbitrary offset.

[ascl:2203.027]
Zoobot: Deep learning galaxy morphology classifier

Walmsley, Mike; Lintott, Chris; Géron, Tobias; Kruk, Sandor; Krawczyk, Coleman; Willett, Kyle W.; Bamford, Steven; Kelvin, Lee S.; Fortson, Lucy; Gal, Yarin; Keel, William; Masters, Karen L.; Mehta, Vihang; Simmons, Brooke D.; Smethurst, Rebecca; Smith, Lewis; Baeten, Elisabeth M.; Macmillan, Christine

Zoobot classifies galaxy morphology with Bayesian CNN. Deep learning models were trained on volunteer classifications; these models were able to both learn from uncertain volunteer responses and predict full posteriors (rather than point estimates) for what volunteers would have said. The code reproduces and improves Galaxy Zoo DECaLS automated classifications, and can be finetuned for new tasks.

[ascl:2203.026]
axionCAMB: Modification of the CAMB Boltzmann code

axionCAMB is a modified version of the publicly available code CAMB (ascl:1102.026). axionCAMB computes cosmological observables for comparison with data. This is normally the CMB power spectra (T,E,B,\phi in auto and cross power), but also includes the matter power spectrum.

[ascl:2203.025]
SetCoverPy: A heuristic solver for the set cover problem

SetCoverPy finds an (near-)optimal solution to the set cover problem (SCP) as fast as possible. It employs an iterative heuristic approximation method, combining the greedy and Lagrangian relaxation algorithms. It also includes a few useful tools for a quick chi-squared fitting given two vectors with measurement errors.

[ascl:2203.024]
Magrathea-Pathfinder: 3D AMR ray-tracing in simulations

Magrathea-Pathfinder propagates photons within cosmological simulations to construct observables. This high-performance framework uses a 3D Adaptive-Mesh Refinement and is built on top of the MAGRATHEA metalibrary (ascl:2203.023).

[ascl:2203.023]
MAGRATHEA: Multi-processor Adaptive Grid Refinement Analysis for THEoretical Astrophysics

MAGRATHEA (Multi-processor Adaptive Grid Refinement Analysis for THEoretical Astrophysics) is a foundational cosmological library and a relativistic raytracing code. Classical linear algebra libraries come with their own operations and can be difficult to leverage for new data types. Instead of providing basic types, MAGRATHEA provides tools to generate base types such as scalar quantities, points, vectors, or tensors.

[ascl:2203.022]
Vetting: Stand-alone tools for vetting transit signals in Kepler, K2 and TESS data

vetting contains simple, stand-alone Python tools for vetting transiting signals in NASA's Kepler, K2, and TESS data. The code performs a centroid test to look for significant changes in the centroid of a star during a transit or eclipse. vetting requires an installation of Python 3.8 or higher.

[ascl:2203.021]
MG-MAMPOSSt: Test gravity with the mass profiles of galaxy clusters

MG-MAMPOSSt extends the MAMPOSSt code (ascl:2203.020), which performs Bayesian fits of models of mass and velocity anisotropy profiles to the distribution of tracers in projected phase space, to handle modified gravity models and constrain its parameters. It implements two distinct types of gravity modifications: general chameleon (including $f(\mathcal{R})$ models), and beyond Horndeski gravity (Vainshtein screening). MG-MAMPOSSt efficently explores the parameter space either by computing the likelihood over a multi-dimensional grid of points or by performing a simple Metropolis-Hastings MCMC. The code requires a Fortran90 compiler or higher and makes use of the getdist package (ascl:1910.018) to plot the marginalized distributions in the MCMC mode.

[ascl:2203.020]
MAMPOSSt: Mass/orbit modeling of spherical systems

MAMPOSSt (Modeling Anisotropy and Mass Profiles of Observed Spherical Systems) is a Bayesian code to perform mass/orbit modeling of spherical systems. It determines marginal parameter distributions and parameter covariances of parametrized radial distributions of dark or total matter, as well as the mass of a possible central black hole, and the radial profiles of density and velocity anisotropy of one or several tracer components, all of which are jointly fit to the discrete data in projected phase space. It is based upon the MAMPOSSt likelihood function for the distribution of individual tracers in projected phase space (projected radius and line-of-sight velocity) and the CosmoMC Markov Chain Monte Carlo code (ascl:1106.025), run in generic mode. MAMPOSSt is not based on the 6D distribution function (which would require triple integrals), but on the assumption that the local 3D velocity distribution is an (anisotropic) Gaussian (requiring only a single integral).

[ascl:2203.019]
agnpy: Modeling jetted Active Galactic Nuclei radiative processes with Python

agnpy focuses on the numerical computation of the photon spectra produced by leptonic radiative processes in jetted Active Galactic Nuclei (AGN). It includes classes describing the galaxy components responsible for line and thermal emission and calculates the absorption due to gamma-gamma pair production on soft (IR-UV) photon fields.

[ascl:2203.018]
sympy2c: Generating fast C/C++ functions and ODE solvers from symbolic expressions

The Python package sympy2c allows creation and compilation of fast C/C++ based extension modules from symbolic representations. It can create fast code for the solution of high dimensional ODEs, or numerical evaluation of integrals where sympy fails to compute an anti-derivative. Based on the symbolic formulation of a stiff ODE, sympy2c analyzes sparsity patterns in the Jacobian matrix of the ODE, and generates loop-less fast code by unrolling loops in the internally used LU factorization algorithm and by avoiding unnecessary computations involving known zeros.

[ascl:2203.017]
MaNGA-DAP: MaNGA Data Analysis Pipeline

Westfall, Kyle B.; Cappellari, Michele; Bershady, Matthew A.; Bundy, Kevin; Belfiore, Francesco; Ji, Xihan; Law, David R.; Schaefer, Adam; Shetty, Shravan; Tremonti, Christy A.; Yan, Renbin; Andrews, Brett H.; Brownstein, Joel R.; Cherinka, Brian; Coccato, Lodovico; Drory, Niv; Maraston, Claudia; Parikh, Taniya; Sánchez-Gallego, José R.; Thomas, Daniel; Weijmans, Anne-Marie; Barrera-Ballesteros, Jorge; Du, Cheng; Goddard, Daniel; Li, Niu; Masters, Karen; Ibarra Medel, Héctor Javier; Sánchez, Sebastián F.; Yang, Meng; Zheng, Zheng; Zhou, Shuang

The MaNGA data analysis pipeline (MaNGA DAP) analyzes the data produced by the MaNGA data-reduction pipeline (ascl:2203.016) to produced physical properties derived from the MaNGA spectroscopy. All survey-provided properties are currently derived from the log-linear binned datacubes (*i.e.*, the LOGCUBE files).

[ascl:2203.016]
MaNGA-DRP: MaNGA Data Reduction Pipeline

Law, David R.; Cherinka, Brian; Yan, Renbin; Andrews, Brett H.; Bershady, Matthew A.; Bizyaev, Dmitry; Blanc, Guillermo A.; Blanton, Michael R.; Bolton, Adam S.; Brownstein, Joel R.; Bundy, Kevin; Chen, Yanmei; Drory, Niv; D'Souza, Richard; Fu, Hai; Jones, Amy; Kauffmann, Guinevere; MacDonald, Nicholas; Masters, Karen L.; Newman, Jeffrey A.; Parejko, John K.; Sánchez-Gallego, José R.; Sánchez, Sebastian F.; Schlegel, David J.; Thomas, Daniel; Wake, David A.; Weijmans, Anne-Marie; Westfall, Kyle B.; Zhang, Kai

The MaNGA Data Reduction Pipeline (DRP) processes the raw data to produce flux calibrated, sky subtracted, coadded data cubes from each of the individual exposures for a given galaxy. The DRP consists of two primary parts: the 2d stage that produces flux calibrated fiber spectra from raw individual exposures, and the 3d stage that combines multiple flux calibrated exposures with astrometric information to produce stacked data cubes. These science-grade data cubes are then processed by the MaNGA Data Analysis Pipeline (ascl:2203.017), which measures the shape and location of various spectral features, fits stellar population models, and performs a variety of other analyses necessary to derive astrophysically meaningful quantities from the calibrated data cubes.

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