P2DFFT is a parallelized version of 2DFFT (ascl:1608.015). It isolates and measures the spiral arm pitch angle of galaxies. The code allows direct input of FITS images, offers the option to output inverse Fourier transform FITS images, and generates idealized logarithmic spiral test images of a specified size that have 1 to 6 arms with pitch angles of -75 degrees to 75 degrees​​. Further, it can output Fourier amplitude versus inner radius and pitch angle versus inner radius for each Fourier component (m = 0 to m = 6), and calculates the Fourier amplitude weighted mean pitch angle across m = 1 to m = 6 versus inner radius.

P2SAD computes the Particle Phase Space Average Density (P2SAD) in galactic haloes. The model for the calculation is based on the stable clustering hypothesis in phase space, the spherical collapse model, and tidal disruption of substructures. The multiscale prediction for P2SAD computed by this IDL code can be used to estimate signals sensitive to the small scale structure of dark matter distributions (e.g. dark matter annihilation). The code computes P2SAD averaged over the whole virialized region of a Milky-Way-size halo at redshift zero.

p3d is semi-automatic data-reduction tool designed to be used with fiber-fed integral-field spectrographs. p3d is a highly general and freely available tool based on IDL but can be used with full functionality without an IDL license. It is easily extended to include improved algorithms, new visualization tools, and support for additional instruments. It uses a novel algorithm for automatic finding and tracing of spectra on the detector, and includes two methods of optimal spectrum extraction in addition to standard aperture extraction. p3d also provides tools to combine several images, perform wavelength calibration and flat field data.

PACCE (Perl Algorithm to Compute continuum and Equivalent Widths) computes continuum and equivalent widths. PACCE is able to determine mean continuum and continuum at line center values, which are helpful in stellar population studies, and is also able to compute the uncertainties in the equivalent widths using photon statistics.

Pacerman, written in IDL, is a new method to calculate Faraday rotation measure maps from multi-frequency polarisation angle data. In order to solve the so called n-pi-ambiguity problem which arises from the observationally ambiguity of the polarisation angle which is only determined up to additions of n times pi, where n is an integer, we suggest using a global scheme. Instead of solving the n-pi-ambiguity for each data point independently, our algorithm, which we chose to call Pacerman solves the n-pi-ambiguity for a high signal-to-noise region "democratically" and uses this information to assist computations in adjacent low signal-to-noise areas.

PACMAN (Planetary Atmosphere, Crust, and MANtle geochemical evolution) runs a coupled redox-geochemical-climate evolution model. It runs Monte Carlo calculations over nominal parameter ranges, including number of iterations and number of cores for parallelization, which can be altered to reproduce different scenarios and sensitivity tests. Model outputs and corresponding input parameters are saved in separate files which are used to plot results; the the user can choose which outputs to plot, including all successful outputs, nominal Earth outputs, waterworld false positives, desertworld false positives, and high CO2:H2O false positives. Among other functions, PACMAN contains functions for interpolating the pre-computed Outgoing Longwave Radiation (OLR) grid, the atmosphere-ocean partitioning grid, and the stratospheric water vapor grid, calculating bond albedo and outgassing fluxes.

PACSman provides an alternative for several reduction and analysis steps performed in HIPE (ascl:1111.001) on PACS spectroscopic data; it is written in IDL. Among the operations possible with it are transient correction, line fitting, map projection, and map analysis, and unchopped scan, chop/nod, and the decommissioned wavelength switching observation modes are supported.

PAHDecomp models mid-infrared spectra of galaxies; it is based on the popular PAHFIT code (ascl:1210.009). In contrast to PAHFIT, this model decomposes the continuum into a star-forming component and an obscured nuclear component based on Bayesian priors on the shape of the star-forming component (using templates + prior on extinction), making this tool ideally suited for modeling the spectra of heavily obscured galaxies. PAHDecomp successfully recovers properties of Compact Obscured Nuclei (CONs) where the inferred nuclear optical depth strongly correlates with the surface brightness of HCN-vib emission in the millimeter. This is currently set up to run on the short low modules of Spitzer IRS data (5.2 - 14.2 microns) but will be ideal for JWST/MIRI MRS data in the future.

PAHFIT is an IDL tool for decomposing Spitzer IRS spectra of PAH emission sources, with a special emphasis on the careful recovery of ambiguous silicate absorption, and weak, blended dust emission features. PAHFIT is primarily designed for use with full 5-35 micron Spitzer low-resolution IRS spectra. PAHFIT is a flexible tool for fitting spectra, and you can add or disable features, compute combined flux bands, change fitting limits, etc., without changing the code.

PAHFIT uses a simple, physically-motivated model, consisting of starlight, thermal dust continuum in a small number of fixed temperature bins, resolved dust features and feature blends, prominent emission lines (which themselves can be blended with dust features), as well as simple fully-mixed or screen dust extinction, dominated by the silicate absorption bands at 9.7 and 18 microns. Most model components are held fixed or are tightly constrained. PAHFIT uses Drude profiles to recover the full strength of dust emission features and blends, including the significant power in the wings of the broad emission profiles. This means the resulting feature strengths are larger (by factors of 2-4) than are recovered by methods which estimate the underlying continuum using line segments or spline curves fit through fiducial wavelength anchors.

The PAL library is a partial re-implementation of Pat Wallace's popular SLALIB library written in C using a Gnu GPL license and layered on top of the IAU's SOFA library (or the BSD-licensed ERFA) where appropriate. PAL attempts to stick to the SLA C API where possible.

Palettable is a library of color palettes for Python. The code is written in pure Python with no dependencies; it can be used to supply color maps for matplotlib plots, customize matplotlib plots, and to supply colors for a web application.

paltas conducts simulation-based inference on strong gravitational lensing images. It builds on lenstronomy (ascl:1804.012) to create large datasets of strong lensing images with realistic low-mass halos, Hubble Space Telescope (HST) observational effects, and galaxy light from HST's COSMOS field. paltas also includes the capability to easily train neural posterior estimators of the parameters of the lensing system and to run hierarchical inference on test populations.

PAMELA is an implementation of the optimal extraction algorithm for long-slit CCD spectroscopy and is well suited for time-series spectroscopy. It properly implements the optimal extraction algorithm for curved spectra, including on-the-fly cosmic ray rejection as well as proper calculation and propagation of the errors. The software is distributed as part of the Starlink software collection (ascl:1110.012).

PampelMuse analyzes integral-field spectroscopic observations of crowded stellar fields and provides several subroutines to perform the individual steps of the data analysis. All analysis steps assume that the IFS data has been properly reduced and that all the instrumental artifacts have been removed. PampelMuse is designed to correctly handle IFS data regardless of which instrument was used to observe the data. In addition to the actual data, the software also requires an estimate of the variances for the analysis; optionally, it can use a bad pixel mask. The analysis relies on the presence of a reference catalogue, containing coordinates and magnitudes of the stars in and around the observed field.

panco2 extracts measurements of the pressure profile of the hot gas inside galaxy clusters from millimeter-wave observations. The extraction is performed using forward modeling the millimeter-wave signal of clusters and MCMC sampling of a posterior distribution for the parameters given the input data. Many characteristic features of millimeter-wave observations can be taken into account, such as filtering (both through PSF smearing and transfer functions), point source contamination, and correlated noise.

PandExo generates instrument simulations of JWST’s NIRSpec, NIRCam, NIRISS and NIRCam and HST WFC3 for planning exoplanet observations. It uses throughput calculations from STScI’s Exposure Time Calculator, Pandeia, and offers both an online tool and a python package.

Pandora searches for exomoons by employing an analytical photodynamical model that includes stellar limb darkening, full and partial planet-moon eclipses, and barycentric motion of planet and moon. The code can be used with nested samplers such as UltraNest (ascl:1611.001) or dynesty (ascl:1809.013). Pandora is fast, calculating 10,000 models and log-likelihood evaluation per second (give or take an order of magnitude, depending on parameters and data); this means that a retrieval with 250 Mio. evaluations until convergence takes about 5 hours on a single core. For searches in large amounts of data, it is most efficient to assign one core per light curve.

Pangloss reconstructs all the mass within a light cone through the Universe. Understanding complex mass distributions like this is important for accurate time delay lens cosmography, and also for accurate lens magnification estimation. It aspires to use all available data in an attempt to make the best of all mass maps.

The PHANGS-ALMA pipeline process data from radio interferometer observations. It uses CASA (ascl:1107.013), AstroPy (ascl:1304.002), and other affiliated packages to process data from calibrated visibilities to science-ready spectral cubes and maps. The PHANGS-ALMA pipeline offers a flexible alternative to the scriptForImaging script distributed by ALMA. The pipeline runs in two separate software environments: CASA 5.6 or 5.7 (staging, imaging and post-processing) and Python 3.6 or later (derived products) with modern versions of several packages.

Modern N-body cosmological simulations contain billions ($10^9$) of dark matter particles. These simulations require hundreds to thousands of gigabytes of memory, and employ hundreds to tens of thousands of processing cores on many compute nodes. In order to study the distribution of dark matter in a cosmological simulation, the dark matter halos must be identified using a halo finder, which establishes the halo membership of every particle in the simulation. The resources required for halo finding are similar to the requirements for the simulation itself. In particular, simulations have become too extensive to use commonly-employed halo finders, such that the computational requirements to identify halos must now be spread across multiple nodes and cores. Here we present a scalable-parallel halo finding method called Parallel HOP for large-scale cosmological simulation data. Based on the halo finder HOP, it utilizes MPI and domain decomposition to distribute the halo finding workload across multiple compute nodes, enabling analysis of much larger datasets than is possible with the strictly serial or previous parallel implementations of HOP. We provide a reference implementation of this method as a part of the toolkit yt, an analysis toolkit for Adaptive Mesh Refinement (AMR) data that includes complementary analysis modules. Additionally, we discuss a suite of benchmarks that demonstrate that this method scales well up to several hundred tasks and datasets in excess of $2000^3$ particles. The Parallel HOP method and our implementation can be readily applied to any kind of N-body simulation data and is therefore widely applicable. Parallel HOP is part of yt.

PARAMESH is a package of Fortran 90 subroutines designed to provide an application developer with an easy route to extend an existing serial code which uses a logically cartesian structured mesh into a parallel code with adaptive mesh refinement (AMR). Alternatively, in its simplest use, and with minimal effort, it can operate as a domain decomposition tool for users who want to parallelize their serial codes, but who do not wish to use adaptivity.

The package builds a hierarchy of sub-grids to cover the computational domain, with spatial resolution varying to satisfy the demands of the application. These sub-grid blocks form the nodes of a tree data-structure (quad-tree in 2D or oct-tree in 3D). Each grid block has a logically cartesian mesh. The package supports 1, 2 and 3D models. PARAMESH is released under the NASA-wide Open-Source software license.

We consider the problem of fitting a parametric model to time-series data that are afflicted by correlated noise. The noise is represented by a sum of two stationary Gaussian processes: one that is uncorrelated in time, and another that has a power spectral density varying as $1/f^gamma$. We present an accurate and fast [O(N)] algorithm for parameter estimation based on computing the likelihood in a wavelet basis. The method is illustrated and tested using simulated time-series photometry of exoplanetary transits, with particular attention to estimating the midtransit time. We compare our method to two other methods that have been used in the literature, the time-averaging method and the residual-permutation method. For noise processes that obey our assumptions, the algorithm presented here gives more accurate results for midtransit times and truer estimates of their uncertainties.

Paramo (PArticle and RAdiation MOnitor) numerically solves the Fokker-Planck kinetic equation, which is used to model the dynamics of a particle distribution function, using a robust implicit method, for the proper modeling of the acceleration processes, and accounts for accurate cooling coefficient (e.g., radiative cooling with Klein-Nishina corrections). The numerical solution at every time step is used to calculate radiations processes, namely synchrotron and IC, with sophisticated numerical techniques, obtaining the multi-wavelength spectral evolution of the system.

ParaMonte contains serial and parallel Monte Carlo routines for sampling mathematical objective functions of arbitrary-dimensions. It is used for posterior distributions of Bayesian models in data science, Machine Learning, and scientific inference and unifies the automation of Monte Carlo simulations. ParaMonte is user friendly and accessible from multiple programming environments, including C, C++, Fortran, MATLAB, and Python, and offers high performance at runtime and scalability across many parallel processors.

ParaView is an open-source, multi-platform data analysis and visualization application. ParaView users can quickly build visualizations to analyze their data using qualitative and quantitative techniques. The data exploration can be done interactively in 3D or programmatically using ParaView's batch processing capabilities.

ParaView was developed to analyze extremely large datasets using distributed memory computing resources. It can be run on supercomputers to analyze datasets of terascale as well as on laptops for smaller data.

PARAVT offers massive parallel computation of Voronoi tessellations (VT hereafter) in large data sets. The code is focused for astrophysical purposes where VT densities and neighbors are widely used. There are several serial Voronoi tessellation codes, however no open source and parallel implementations are available to handle the large number of particles/galaxies in current N-body simulations and sky surveys. Parallelization is implemented under MPI and VT using Qhull library. Domain decomposition take into account consistent boundary computation between tasks, and support periodic conditions. In addition, the code compute neighbors lists, Voronoi density and Voronoi cell volumes for each particle, and can compute density on a regular grid.

PARS (Paint the Atmospheres of Rotating Stars) quickly computes magnitudes and spectra of rotating stellar models. It uses the star's mass, equatorial radius, rotational speed, luminosity, and inclination as input; the models incorporate Roche mass distribution (where all mass is at the center of the star), solid body rotation, and collinearity of effective gravity and energy flux.

PARSEC (PARametrized Simulation Engine for Cosmic rays) is a simulation engine for fast generation of ultra-high energy cosmic ray data based on parameterizations of common assumptions of UHECR origin and propagation. Implemented are deflections in unstructured turbulent extragalactic fields, energy losses for protons due to photo-pion production and electron-pair production, as well as effects from the expansion of the universe. Additionally, a simple model to estimate propagation effects from iron nuclei is included. Deflections in the Galactic magnetic field are included using a matrix approach with precalculated lenses generated from backtracked cosmic rays. The PARSEC program is based on object oriented programming paradigms enabling users to extend the implemented models and is steerable with a graphical user interface.

ParselTongue is a Python interface to classic AIPS, Obit and possibly other task-based data reduction packages. It serves as the software infrastructure for some of the ALBUS implementation. It allows you to run AIPS tasks, and access AIPS headers and extension tables from Python. There is also support for running Obit tasks and accessing data in FITS files. Full access to the visibilities in AIPS UV data is also available.

ParSNIP learns generative models of transient light curves from a large dataset of transient light curves. It is designed to work with light curves in sncosmo format using the lcdata package to handle large datasets. This code can be used for classification of transients, cosmological distance estimation, and identifying novel transients.

The Parthenon framework, derived from Athena++ (ascl:1912.005), handles massively-parallel, device-accelerated adaptive mesh refinement. It provides a device first/device resident approach, transparent packing of data across blocks (to reduce/hide kernel launch latency), and direct device-to-device communication via asynchronous, one-sided MPI communication to enable high performance. Parthenon uses an intermediate abstraction layer to hide complexity of device kernel launches, offers support for particles and abstract variable control via metadata tags, and has a flexible plug-in package system.

Piernik is a multi-fluid grid magnetohydrodynamic (MHD) code based on the Relaxing Total Variation Diminishing (RTVD) conservative scheme. The original code has been extended by addition of dust described within the particle approximation. The dust is now described as a system of interacting particles. The particles can interact with gas, which is described as a fluid. The comparison between the test problem results and the results coming from fluid simulations made with Piernik code shows the most important differences between fluid and particle approximations used to describe dynamical evolution of dust under astrophysical conditions.

ParticleGridMapper.jl interpolates particle data onto either a Cartesian (uniform) grid or an adaptive mesh refinement (AMR) grid where each cell contains no more than one particle. The AMR grid can be trimmed with a user-defined maximum level of refinement. Three different interpolation schemes are supported: nearest grid point (NGP), smoothed-particle hydrodynamics (SPH), and Meshless finite mass (MFM). It is multi-threading parallel.

In dense clusters a bewildering variety of interactions between stars can be observed, ranging from simple encounters to collisions and other mass-transfer encounters. With faster and special-purpose computers like GRAPE, the amount of data per simulation is now exceeding 1TB. Visualization of such data has now become a complex 4D data-mining problem, combining space and time, and finding interesting events in these large datasets. We have recently starting using the virtual reality simulator, installed in the Hayden Planetarium in the American Museum for Natural History, to tackle some of these problem. partiview is a program that enables you to visualize and animate particle data. partiview runs on relatively simple desktops and laptops, but is mostly compatible with its big brother VirDir.

PASTA performs median stacking of astronomical sources. Written in Python, it can filter sources, provide stack statistics, generate Karma annotations, format source lists, and read information from stacked Flexible Image Transport System (FITS) images. PASTA was originally written to examine polarization stack properties and includes a Monte Carlo modeler for obtaining true polarized intensity from the observed polarization of a stack. PASTA is also useful as a generic stacking tool, even if polarization properties are not being examined.

We present Particle-Based Lensing (PBL), a new technique for gravitational lensing mass reconstructions of galaxy clusters. Traditionally, most methods have employed either a finite inversion or gridding to turn observational lensed galaxy ellipticities into an estimate of the surface mass density of a galaxy cluster. We approach the problem from a different perspective, motivated by the success of multi-scale analysis in smoothed particle hydrodynamics. In PBL, we treat each of the lensed galaxies as a particle and then reconstruct the potential by smoothing over a local kernel with variable smoothing scale. In this way, we can tune a reconstruction to produce constant signal-noise throughout, and maximally exploit regions of high information density.

PBL is designed to include all lensing observables, including multiple image positions and fluxes from strong lensing, as well as weak lensing signals including shear and flexion. In this paper, however, we describe a shear-only reconstruction, and apply the method to several test cases, including simulated lensing clusters, as well as the well-studied ``Bullet Cluster'' (1E0657-56). In the former cases, we show that PBL is better able to identify cusps and substructures than are grid-based reconstructions, and in the latter case, we show that PBL is able to identify substructure in the Bullet Cluster without even exploiting strong lensing measurements.

PBMC (Pre-Conditioned Backward Monte Carlo) solves the vector Radiative Transport Equation (vRTE) and can be applied to planetary atmospheres irradiated from above. The code builds the solution by simulating the photon trajectories from the detector towards the radiation source, i.e. in the reverse order of the actual photon displacements. In accounting for the polarization in the sampling of photon propagation directions and pre-conditioning the scattering matrix with information from the scattering matrices of prior (in the BMC integration order) photon collisions, PBMC avoids the unstable and biased solutions of classical BMC algorithms for conservative, optically-thick, strongly-polarizing media such as Rayleigh atmospheres.

The equation of state (EOS) of dense matter is a crucial input for the neutron-star structure calculations. This Fortran code can obtain a "unified EOS" in the many-body calculations based on a single effective nuclear Hamiltonian, and is valid in all regions of the neutron star interior. For unified EOSs, the transitions between the outer crust and the inner crust and between the inner crust and the core are obtained as a result of many-body calculations.

The mid-infrared spectra of ultraluminous infrared galaxies (ULIRGs) contain a variety of spectral features that can be used as diagnostics to characterize the spectra. However, such diagnostics are biased by our prior prejudices on the origin of the features. Moreover, by using only part of the spectrum they do not utilize the full information content of the spectra. Blind statistical techniques such as principal component analysis (PCA) consider the whole spectrum, find correlated features and separate them out into distinct components.

This code, written in IDL, classifies principal components of IRS spectra to define a new classification scheme using 5D Gaussian mixtures modelling. The five PCs and average spectra for the four classifications to classify objects are made available with the code.

PCAT (Probabilistic Cataloger) samples from the posterior distribution of a metamodel, i.e., union of models with different dimensionality, to compare the models. This is achieved via transdimensional proposals such as births, deaths, splits and merges in addition to the within-model proposals. This method avoids noisy estimates of the Bayesian evidence that may not reliably distinguish models when sampling from the posterior probability distribution of each model.

The code has been applied in two different subfields of astronomy: high energy photometry, where transdimensional elements are gamma-ray point sources; and strong lensing, where light-deflecting dark matter subhalos take the role of transdimensional elements.

PCCDPACK analyzes polarimetry data. The set of routines is written in CL-IRAF (including compiled Fortran codes) and analyzes dozens of point objects simultaneously on the same CCD image. A subpackage, specpol, is included to analyze spectropolarimetry data.

The Periodogram Comparison for Optimizing Small Transiting Planet Detection R code compares two periodogram algorithms for detecting transiting exoplanets: the Box-fitting Least Squares (BLS) and the Transit Comb Filter (TCF). It calculates the False Alarm Probability (FAP) based on extreme value theory and signal-to-noise ratio (SNR) metrics to quantify periodogram peak significance. The comparison approach is aimed at optimizing the detection of small transiting planets in future transiting exoplanet surveys. The code can be extended for comparing any set of periodograms.

PDFchem models the cold ISM at moderate and large scales using functions connecting the quantities of the local and the observed visual extinctions and the local number density with probability density functions. For any given observed visual extinction sampled with thousands of clouds, the algorithm instantly computes the average abundances of the most important species and performs radiative transfer calculations to estimate the average emission of the most commonly observed lines.

PDM2 (Phase Dispersion Minimization) ddetermines periodic components of data sets with erratic time intervals, poor coverage, non-sine-wave curve shape, and/or large noise components. Essentially a least-squares fitting technique, the fit is relative to the mean curve as defined by the means of each bin; the code simultaneously obtains the best least-squares light curve and the best period. PDM2 allows an arbitrary degree of smoothing and provides improved curve fits, suppressed subharmonics, and beta function statistics.

Ultraviolet photons from O and B stars strongly influence the structure and emission spectra of the interstellar medium. The UV photons energetic enough to ionize hydrogen (hν > 13.6 eV) will create the H II region around the star, but lower energy UV photons escape. These far-UV photons (6 eV < hν < 13.6 eV) are still energetic enough to photodissociate molecules and to ionize low ionization-potential atoms such as carbon, silicon, and sulfur. They thus create a photodissociation region (PDR) just outside the H II region. In aggregate, these PDRs dominate the heating and cooling of the neutral interstellar medium.

The PDR Toolbox is a science-enabling Python package for the community, designed to help astronomers determine the physical parameters of photodissociation regions from observations. Typical observations of both Galactic and extragalactic PDRs come from ground- and space-based millimeter, submillimeter, and far-infrared telescopes such as ALMA, SOFIA, JWST, Spitzer, and Herschel. Given a set of observations of spectral line or continuum intensities, PDR Toolbox can compute best-fit FUV incident intensity and cloud density based on our models of PDR emission.

pdspy fits Monte Carlo radiative transfer models for protostellar/protoplanetary disks to ALMA continuum and spectral line datasets using Markov Chain Monte Carlo fitting. It contains two tools, one to fit ALMA continuum visibilities and broadband spectral energy distributions (SEDs) with full radiative transfer models, and another to fit ALMA spectral line visibilities with protoplanetary disk models that include a vertically isothermal, power law temperature distribution. No radiative equilibrium calculation is done.

PDT removes systematic trends in light curves. It finds clusters of light curves that are highly correlated using machine learning, constructs one master trend per cluster and detrends an individual light curve using the constructed master trends by minimizing residuals while constraining coefficients to be positive.

The NVIDIA GPU-based pipeline code peasoup provides a one-step pulsar search, including searching for pulsars with up to moderate accelerations, with only one command. Its features include dedispersion, dereddening in the Fourier domain, resampling, peak detection, and optional time series folding. peasoup's output is the candidate list.

The PEC (Period Error Calculator) algorithm estimates the period error for eclipsing binaries observed by the Kepler Mission. The algorithm is based on propagation of error theory and assumes that observation of every light curve peak/minimum in a long time-series observation can be unambiguously identified. A simple C implementation of the PEC algorithm is available.

PÉGASE-HR is a code aimed at computing synthetic evolutive optical spectra of galaxies with a very high resolution (R=10 000, or dlambda=0.55) in the range Lambda=[4000, 6800] Angstroms. PÉGASE-HR is the result of combining the code PÉGASE.2 with the high-resolution stellar library ÉLODIE. This code can also be used at low resolution (R=200) over the range covered by the BaSeL library (from far UV to the near IR), and then produces the same results as PÉGASE.2. In PEGASE-HR, the BaSeL library is replaced by a grid of spectra interpolated from the high-resolution ÉLODIE library of stellar spectra. The ÉLODIE library is a stellar database of 1959 spectra for 1503 stars, observed with the echelle spectrograph ÉLODIE on the 193 cm telescope at the Observatoire de Haute Provence.

PÉGASE (Projet d'Étude des GAlaxies par Synthèse Évolutive) is a code to compute the spectral evolution of galaxies. The evolution of the stars, gas and metals is followed for a law of star formation and a stellar initial mass function. The stellar evolutionary tracks extend from the main sequence to the white dwarf stage. The emission of the gas in HII regions is also taken into account. The main improvement in version 2 is the use of evolutionary tracks of different metallicities (from 10-4 to 5×solar). The effect of extinction by dust is also modelled using a radiative transfer code. PÉGASE.2 uses the BaSeL library of stellar spectra and can therefore synthesize low-resolution (R~200) ultraviolet to near-infrared spectra of Hubble sequence galaxies as well as of starbursts.

Pelican is an efficient, lightweight C++ library for quasi-real time data processing. The library provides a framework to separate the acquisition and processing of data, allowing the scalability and flexibility to fit a number of scenarios. Though its origin was in radio astronomy, processing data as it arrives from a telescope, the framework is sufficiently generic to be useful to any application that requires the efficient processing of incoming data streams.

The Pencil code is a high-order finite-difference code for compressible hydrodynamic flows with magnetic fields. It is highly modular and can easily be adapted to different types of problems. The code runs efficiently under MPI on massively parallel shared- or distributed-memory computers, like e.g. large Beowulf clusters. The Pencil code is primarily designed to deal with weakly compressible turbulent flows. To achieve good parallelization, explicit (as opposed to compact) finite differences are used. Typical scientific targets include driven MHD turbulence in a periodic box, convection in a slab with non-periodic upper and lower boundaries, a convective star embedded in a fully nonperiodic box, accretion disc turbulence in the shearing sheet approximation, self-gravity, non-local radiation transfer, dust particle evolution with feedback on the gas, etc. A range of artificial viscosity and diffusion schemes can be invoked to deal with supersonic flows. For direct simulations regular viscosity and diffusion is being used. The code is written in well-commented Fortran90.

PENTACLE calculates gravitational interactions between particles within a cut-off radius and a Barnes-Hut tree method for gravity from particles beyond. It uses FDPS (ascl:1604.011) to parallelize a Barnes-Hut tree algorithm for a memory-distributed supercomputer. The software can handle 1-10 million particles in a high-resolution N-body simulation on CPU clusters for collisional dynamics, including physical collisions in a planetesimal disc.

Planetary Ephemeris Program (PEP) computes numerical ephemerides and simultaneously analyzes a heterogeneous collection of astrometric data. Written in Fortran, it is a general-purpose astrometric data-analysis program and models orbital motion in the solar system, determines orbital initial conditions and planetary masses, and has been used to, for example, measure general relativistic effects and test physics theories beyond the standard model. PEP also models pulsar motions and distant radio sources, and can solve for sky coordinates for radio sources, plasma densities, and the second harmonic of the Sun's gravitational field.

PEPITA (Prediction of Exoplanet Precisions using Information in Transit Analysis) makes predictions for the precision of exoplanet parameters using transit light-curves. The code uses information analysis techniques to predict the best precision that can be obtained by fitting a light-curve without actually needing to perform the fit, thus allowing more efficient planning of observations or re-observations.

PEREGRINE performs full parameter estimation on gravitational wave signals. Using an internal Truncated Marginal Neural Ratio Estimation (TMNRE) algorithm and building upon the swyft (ascl:2302.016) code to efficiently access marginal posteriors, PEREGRINE conducts a sequential simulation-based inference approach to support the analysis of both transient and continuous gravitational wave sources. The code can fully reconstruct the posterior distributions for all parameters of spinning, precessing compact binary mergers using waveform approximants.

perfectns performs dynamic nested sampling and standard nested sampling for spherically symmetric likelihoods and priors, and analyses the samples produced. The spherical symmetry allows the nested sampling algorithm to be followed “perfectly” - i.e. without implementation-specific errors correlations between samples. It is intended for use in research into the statistical properties of nested sampling, and to provide a benchmark for testing the performance of nested sampling software packages used for practical problems - which rely on numerical techniques to produce approximately uncorrelated samples.

PERIOD searches for periodicities in data. It is distributed within the Starlink software collection (ascl:1110.012).

Period04 statistically analyzes large astronomical time series containing gaps. It calculates formal uncertainties, can extract the individual frequencies from the multiperiodic content of time series, and provides a flexible interface to perform multiple-frequency fits with a combination of least-squares fitting and the discrete Fourier transform algorithm. Period04, written in Java/C++, supports the SAMP communication protocol to provide interoperability with other applications of the Virtual Observatory. It is a reworked and extended version of Period98 (Sperl 1998) and PERIOD/PERDET (Breger 1990).

The N-body code PETAR (ParticlE Tree & particle-particle & Algorithmic Regularization) combines the methods of Barnes-Hut tree, Hermite integrator and slow-down algorithmic regularization (SDAR). It accurately handles an arbitrary fraction of multiple systems (e.g. binaries, triples) while keeping a high performance by using the hybrid parallelization methods with MPI, OpenMP, SIMD instructions and GPU. PETAR has very good agreement with NBODY6++GPU results on the long-term evolution of the global structure, binary orbits and escapers and is significantly faster when used on a highly configured GPU desktop computer. PETAR scales well when the number of cores increase on the Cray XC50 supercomputer, allowing a solution to the ten million-body problem which covers the region of ultra compact dwarfs and nuclear star clusters.

petitRADTRANS (pRT) calculates transmission and emission spectra of exoplanets for clear and cloudy planets. It also incorporates an easy subpackage for running retrievals with nested sampling. It allows the calculation of emission or transmission spectra, at low or high resolution, clear or cloudy, and includes a retrieval module to fit a petitRADTRANS model to spectral data. pRT has two different opacity treatment modes. The low resolution mode runs calculations at λ/Δλ ≤ 1000 using the so-called correlated-k treatment for opacities. The high resolution mode runs calculations at λ/Δλ ≤ 10⁶, using a line-by-line opacity treatment.

PetroFit calculates Petrosian properties, such as radii and concentration indices; it also fits galaxy light profiles. The package, built on Photutils (ascl:1609.011), includes tools for performing accurate photometry, segmentations, Petrosian properties, and fitting.

PETSc (Portable, Extensible Toolkit for Scientific Computation) provides a suite of data structures and routines for the scalable (parallel) solution of scientific applications modeled by partial differential equations, and is intended for use in large-scale application projects. The toolkit includes a large suite of parallel linear, nonlinear equation solvers and ODE integrators that are easily used in application codes written in C, C++, Fortran and Python. PETSc provides many of the mechanisms needed within parallel application codes, such as simple parallel matrix and vector assembly routines that allow the overlap of communication and computation. In addition, PETSc (pronounced PET-see) includes support for managing parallel PDE discretizations.

PEXO provides a global modeling framework for ns timing, μas astrometry, and μm/s radial velocities. It can account for binary motion and stellar reflex motions induced by planetary companions and also treat various relativistic effects both in the Solar System and in the target system (Roemer, Shapiro, and Einstein delays). PEXO is able to model timing to a precision of 1 ns, astrometry to a precision of 1 μas, and radial velocity to a precision of 1 μm/s.

PFANT computes a synthetic spectrum assuming local thermodynamic equilibrium from a given stellar model atmosphere and lists of atomic and molecular lines; it provides large wavelength coverage and line lists from ultraviolet through the visible and near-infrared. PFANT has been optimized for speed, offers error reporting, and command-line configuration options.

pfits reads, manipulates and processes PSRFITS format search- and fold-mode pulsar astronomy data files. It summerizes the header information in a PSRFITS file, reproduces some of fv's (ascl:1205.005) functionality, and allows the user to obtain detailed information about the file. It can determine whether the data is search mode or fold mode and plot the profile, color scale image, frequency time, sum in frequency, and 4-pol data, as appropriate. pfits can also read in a search mode file, dedisperses, and frequency-sums (if requested), and offers an option to output multiple dispersed data files, among other tasks.

PFITS performs data reduction of spectra, including dark removal and flat fielding; this software was a standard 1983 Reticon reduction package available at the University of Texas. It was based on the plotting program PCOSY by Gary Ferland, and in 1985 was updated by Andrew McWilliam.

PGOPHER simulates and fits rotational, vibrational, and electronic spectra. It handles linear molecules and symmetric and asymmetric tops, including effects due to unpaired electrons and nuclear spin, with a separate mode for vibrational structure. The code performs many sorts of transitions, including Raman, multiphoton, and forbidden transitions. It can simulate multiple species and states simultaneously, including special effects such as perturbations and state dependent predissociation. Fitting can be to line positions, intensities, or band contours. PGOPHER uses a standard graphical user interface and makes comparison with, and fitting to, spectra from various sources easy. In addition to overlaying numerical spectra, it is also possible to overlay pictures from pdf files and even plate spectra to assist in checking that published constants are being used correctly.

The PGPLOT Graphics Subroutine Library is a Fortran- or C-callable, device-independent graphics package for making simple scientific graphs. It is intended for making graphical images of publication quality with minimum effort on the part of the user. For most applications, the program can be device-independent, and the output can be directed to the appropriate device at run time.

The PGPLOT library consists of two major parts: a device-independent part and a set of device-dependent "device handler" subroutines for output on various terminals, image displays, dot-matrix printers, laser printers, and pen plotters. Common file formats supported include PostScript and GIF.

PGPLOT itself is written mostly in standard Fortran-77, with a few non-standard, system-dependent subroutines. PGPLOT subroutines can be called directly from a Fortran-77 or Fortran-90 program. A C binding library (cpgplot) and header file (cpgplot.h) are provided that allow PGPLOT to be called from a C or C++ program; the binding library handles conversion between C and Fortran argument-passing conventions.

Phantom-GRAPE is a numerical software library to accelerate collisionless $N$-body simulation with SIMD instruction set on x86 architecture. The Newton's forces and also central forces with an arbitrary shape f(r), which have a finite cutoff radius r_cut (i.e. f(r)=0 at r>r_cut), can be quickly computed.

Phantom is a smoothed particle hydrodynamics and magnetohydrodynamics code focused on stellar, galactic, planetary, and high energy astrophysics. It is modular, and handles sink particles, self-gravity, two fluid and one fluid dust, ISM chemistry and cooling, physical viscosity, non-ideal MHD, and more. Its modular structure makes it easy to add new physics to the code.

phase_space_cosmo_fisher produces Fisher matrix 2D contours from which the constraints on cosmological parameters can be derived. Given a specified redshift array and cosmological case, 2D marginalized contours of cosmological parameters are generated; the code can also plot the derivatives used in the Fisher matrix. In addition, this package can generate 3D plots of qH^2 and other cosmological quantities as a function of redshift and cosmology.

PhaseTracer maps out cosmological phases, and potential transitions between them, for Standard Model extensions with any number of scalar fields. The code traces the minima of effective potential as the temperature changes, and then calculates the critical temperatures at which the minima are degenerate. PhaseTracer can use potentials provided by other packages and can be used to analyze cosmological phase transitions which played an important role in the early evolution of the Universe.

PhAst (Photometry-Astrometry) is an IDL astronomical image viewer based on the existing application ATV which displays and analyzes FITS images. It can calibrate raw images, provide astrometric solutions, and do circular aperture photometry. PhAst allows the user to load, process, and blink any number of images. Analysis packages include image calibration, photometry, and astrometry (provided through an interface with SExtractor, SCAMP, and missFITS). PhAst has been designed to generate reports for Minor Planet Center reporting.

Persistent_Homology_LSS analyzes halo catalogs using persistent homology to constrain cosmological parameters. It implements persistent homology on a point cloud composed of halos positions in a cubic box from N-body simulations of the universe at large scales. The output of the code are persistence diagrams and images that are used to constrain cosmological parameters from the halo catalog.

PHOEBE (PHysics Of Eclipsing BinariEs) is a modeling package for eclipsing binary stars, built on top of the widely used WD program (Wilson & Devinney 1971). This introductory paper overviews most important scientific extensions (incorporating observational spectra of eclipsing binaries into the solution-seeking process, extracting individual temperatures from observed color indices, main-sequence constraining and proper treatment of the reddening), numerical innovations (suggested improvements to WD's Differential Corrections method, the new Nelder & Mead's downhill Simplex method) and technical aspects (back-end scripter structure, graphical user interface). While PHOEBE retains 100% WD compatibility, its add-ons are a powerful way to enhance WD by encompassing even more physics and solution reliability.

PHOENIX is a general-purpose state-of-the-art stellar and planetary atmosphere code. It can calculate atmospheres and spectra of stars all across the HR-diagram including main sequence stars, giants, white dwarfs, stars with winds, TTauri stars, novae, supernovae, brown dwarfs and extrasolar giant planets.

The Photon Simulator (PhoSim) is a set of fast photon Monte Carlo codes used to calculate the physics of the atmosphere, telescope, and detector by using modern numerical techniques applied to comprehensive physical models. PhoSim generates images by collecting photons into pixels. The code takes the description of what astronomical objects are in the sky at a particular time (the instance catalog) as well as the description of the observing configuration (the operational parameters) and produces a realistic data stream of images that are similar to what a real telescope would produce. PhoSim was developed for large aperture wide field optical telescopes, such as the planned design of LSST. The initial version of the simulator also targeted the LSST telescope and camera design, but the code has since been broadened to include existing telescopes of a related nature. The atmospheric model, in particular, includes physical approximations that are limited to this general context.

Photo-z-SQL is a flexible template-based photometric redshift estimation framework that can be seamlessly integrated into a SQL database (or DB) server and executed on demand in SQL. The DB integration eliminates the need to move large photometric datasets outside a database for redshift estimation, and uses the computational capabilities of DB hardware. Photo-z-SQL performs both maximum likelihood and Bayesian estimation and handles inputs of variable photometric filter sets and corresponding broad-band magnitudes.

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.

Photodynam facilitates so-called "photometric-dynamical" modeling. This model is quite simple and this is reflected in the code base. A N-body code provides coordinates and the photometric code produces light curves based on coordinates.

PHOTOe simulates the slowing down of photoelectrons in a gas with arbitrary amounts of H, He and O atoms, and thermal electrons, making PHOTOe useful for investigating the atmospheres of exoplanets. The multi-score scheme used in this code differs from other Monte Carlo approaches in that it efficiently handles rare collisional channels, as in the case of low-abundance excited atoms that undergo superelastic and inelastic collisions. PHOTOe outputs include production and energy yields, steady-state photoelectron flux, and estimates of the 'relaxation' time required by the photoelectrons to slow down from the injection energy to the cutoff energy. The model can also estimate the pathlength travelled by the photoelectrons while relaxing.

PHOTOM performs photometry of digitized images. It has two basic modes of operation: using an interactive display to specify the positions for the measurements, or obtaining those positions from a file. In both modes of operation PHOTOM performs photometry using either the traditional aperture method or via optimal extraction. When using the traditional aperture extraction method the target aperture can be circular or elliptical and its size and shape can be varied interactively on the display, or by entering values from the keyboard. Both methods allow the background sky level to be either sampled interactively by the manual positioning of an aperture, or automatically from an annulus surrounding the target object. PHOTOM is the photometry backend for the GAIA tool (ascl:1403.024) and is part of the Starlink software collection (ascl:1110.012).

PHOTOMETRYPIPELINE (PP) provides calibrated photometry from imaging data obtained with small to medium-sized observatories. PP uses Source Extractor (ascl:1010.064) and SCAMP (ascl:1010.063) to register the image data and perform aperture photometry. Calibration is obtained through matching of field stars with reliable photometric catalogs. PP has been specifically designed for the measurement of asteroid photometry, but can also be used to obtain photometry of fixed sources.

Photon makes simple 1D plots in python. It uses mainly matplotlib and PyQt5 and has been build to be fully customizable, allowing the user to change the fontstyle, fontsize, fontcolors, linewidth of the axes, thickness, and other parameters, and see the changes directly in the plot. Once a customization is created, it can be saved in a configuration file and reloaded for future use, allowing reuse of the customization for other plots. The main tool is a graphical user interface and it is started using a command line interface.

PhotoParallax calculates photometric parallaxes for distant stars in the Gaia TGAS catalog without any use of physical stellar models or stellar density models of the Milky Way. It uses the geometric parallaxes to calibrate a photometric model that is purely statistical, which is a model of the data rather than a model of stars per se.

PhotoRApToR (PHOTOmetric Research APplication TO Redshifts) solves regression and classification problems and is specialized for photo-z estimation. PhotoRApToR offers data table manipulation capabilities and 2D and 3D graphics tools for data visualization; it also provides a statistical report for both classification and regression experiments. The code is written in Java; the machine learning model is in C++ to increase the core execution speed.

Photutils provides tools for detecting and performing photometry of astronomical sources. It can estimate the background and background rms in astronomical images, detect sources in astronomical images, estimate morphological parameters of those sources (e.g., centroid and shape parameters), and perform aperture and PSF photometry. Written in Python, it is an affiliated package of Astropy (ascl:1304.002).

PHOX is a novel, virtual X-ray observatory designed to obtain synthetic observations from hydro-numerical simulations. The code is a photon simulator and can be apply to simulate galaxy clusters. In fact, X-ray observations of clusters of galaxies continue to provide us with an increasingly detailed picture of their structure and of the underlying physical phenomena governing the gaseous component, which dominates their baryonic content. Therefore, it is fundamental to find the most direct and faithful way to compare such observational data with hydrodynamical simulations of cluster-like objects, which can currently include various complex physical processes. Here, we present and analyse synthetic Suzaku observations of two cluster-size haloes obtained by processing with PHOX the hydrodynamical simulation of the large-scale, filament-like region in which they reside. Taking advantage of the simulated data, we test the results inferred from the X-ray analysis of the mock observations against the underlying, known solution. Remarkably, we are able to recover the theoretical temperature distribution of the two haloes by means of the multi-temperature fitting of the synthetic spectra. Moreover, the shapes of the reconstructed distributions allow us to trace the different thermal structure that distinguishes the dynamical state of the two haloes.

Plages Identification identifies solar plages from Ca II K photographic observations irrespective of noise level, brightness, and other image properties. The code provides an efficient, reliable method for identifying solar plages. The output of the algorithm is an image highlighting the plages and the calculated plage index. Plages Identification is also deployed as a webapp, allowing users to experiment with different hyperparameters and visualize their impact on the output image in real time.

ISOPHOT is one of the instruments on board the Infrared Space Observatory (ISO). ISOPHOT Interactive Analysis (PIA) is a scientific and calibration interactive data analysis tool for ISOPHOT data reduction. Written in IDL under Xwindows, PIA offers a full context sensitive graphical interface for retrieving, accessing and analyzing ISOPHOT data. It is available in two nearly identical versions; a general observers version omits the calibration sequences.

PIAO is an efficient memory-controlled Python code that uses the standard spherical overdensity (SO) algorithm to identify halos. PIAO employs two additional parameters besides the overdensity Δc. The first is the mesh-box size, which splits the whole simulation box into smaller ones then analyzes them one-by-one, thereby overcoming a possible memory limitation problem that can occur when dealing with high-resolution, large-volume simulations. The second is the smoothed particle hydrodynamics (SPH) neighbors number, which is used for the SPH density calculation.

PICASO (Planetary Intensity Code for Atmospheric Scattering Observations), written in Python, computes the reflected light of exoplanets at any phase geometry using direct and diffuse scattering phase functions and Raman scattering spectral features.

picca fits continua of forests, computes correlation functions (1D and 3D) and power-spectra (1D), computes covariance matrices, and fits models for the correlation functions. This set of tools is used for the analysis of the Lyman-alpha forest sample from the extended Baryon Oscillation Spectroscopic Survey (eBOSS) and the Dark Energy Spectroscopic Instrument (DESI).

Piccard is a Bayesian-inference pipeline for Pulsar Timing Array (PTA) data and interacts with Tempo2 (ascl:1210.015) through libstempo (ascl:2002.017). The code is used mainly for single-pulsar analysis and gravitational-wave detection purposes of full Pulsar Timing Array datasets. Modeling of the data can include correlated signals per frequency or modeled spectrum, with uniform, dipolar, quadrupolar, or anisotropic correlations; multiple error bars and EFACs per pulsar; and white and red noise. Timing models can be numerically included, either by using the design matrix (linear timing model), or by calling libstempo for the full non-linear timing model. Many types of samplers are included. For common-mode mitigation, the signals can be reconstructed mitigating arbitrary signals simultaneously.

Pico is an algorithm that quickly computes the CMB scalar, tensor and lensed power spectra, the matter transfer function and the WMAP 5 year likelihood. It is intended to accelerate parameter estimation codes; Pico can compute the CMB power spectrum and matter transfer function, as well as any computationally expensive likelihoods, in a few milliseconds. It is extremely fast and accurate over a large volume of parameter space and its accuracy can be improved by using a larger training set. More generally, Pico allows using massively parallel computing resources, including distributed computing projects such as Cosmology@Home, to speed up the slow steps in inherently sequential calculations.

PICsar simulates the magnetosphere of an aligned axisymmetric pulsar and can be used to simulate other arbitrary electromagnetics problems in axisymmetry. Written in Fortran, this special relativistic, electromagnetic, charge conservative particle in cell code features stretchable body-fitted coordinates that follow the surface of a sphere, simplifying the application of boundary conditions in the case of the aligned pulsar; a radiation absorbing outer boundary, which allows a steady state to be set up dynamically and maintained indefinitely from transient initial conditions; and algorithms for injection of charged particles into the simulation domain. PICsar is parallelized using MPI and has been used on research problems with ~1000 CPUs.

pieflag compares bandpass-calibrated data to a clean reference channel and identifies and flags essentially all bad data. pieflag compares visibility amplitudes in each frequency channel to a 'reference' channel that is rfi-free (or manually ensured to be rfi-free). pieflag performs this comparison independently for each correlation on each baseline, but will flag all correlations if threshold conditions are met. To operate effectively, pieflag must be supplied with bandpass-calibrated data. pieflag has two core modes of operation (static and dynamic flagging) with an additional extend mode; the type of data largely determines which mode to choose. Instructions for pre-processing data and selecting the mode of operation are provided in the help file. Once pre-processing and selecting the mode of operation are done, pieflag should work well 'out of the box' with its default parameters.

Piff models the point-spread function (PSF) across multiple detectors in the full field of view (FOV). Models can be built in chip coordinates or in sky coordinates if needed to account for the effects of astrometric distortion. The software can fit in either real or Fourier space, and can identify and excise outlier stars that are poor exemplars of the PSF according to some metric.