pPXF is an IDL (and free GDL or FL) program which extracts the stellar kinematics or stellar population from absorption-line spectra of galaxies using the Penalized Pixel-Fitting method (pPXF) developed by Cappellari & Emsellem (2004, PASP, 116, 138). Additional features implemented in the pPXF routine include:
PRECESSION is a comprehensive toolbox for exploring the dynamics of precessing black-hole binaries in the post-Newtonian regime. It allows study of the evolution of the black-hole spins along their precession cycles, performs gravitational-wave-driven binary inspirals using both orbit-averaged and precession-averaged integrations, and predicts the properties of the merger remnant through fitting formulas obtained from numerical-relativity simulations. PRECESSION can add the black-hole spin dynamics to larger-scale numerical studies such as gravitational-wave parameter estimation codes, population synthesis models to predict gravitational-wave event rates, galaxy merger trees and cosmological simulations of structure formation, and provides fast and reliable integration methods to propagate statistical samples of black-hole binaries from/to large separations where they form to/from small separations where they become detectable, thus linking gravitational-wave observations of spinning black-hole binaries to their astrophysical formation history. The code is also useful for computing initial parameters for numerical-relativity simulations targeting specific precessing systems.
pred_loggs models the entire PGF probability density field, enabling iterative statistical modeling of upper limits and prediction of full G/S probability distributions for individual galaxies.
PREDICT is an open-source, multi-user satellite tracking and orbital prediction program written under the Linux operating system. PREDICT provides real-time satellite tracking and orbital prediction information to users and client applications through:
Pressure-Entropy SPH, a modified version of GADGET-2, uses the Lagrangian “Pressure-Entropy” formulation of the SPH equations. This removes the spurious “surface tension” force substantially improving the treatment of fluid mixing and contact discontinuities. Pressure-Entropy SPH shows good performance in mixing experiments (e.g. Kelvin-Helmholtz & blob tests), with conservation maintained even in strong shock/blastwave tests, where formulations without manifest conservation produce large errors. This improves the treatment of sub-sonic turbulence and lessens the need for large kernel particle numbers.
PRESTO is a large suite of pulsar search and analysis software. It was primarily designed to efficiently search for binary millisecond pulsars from long observations of globular clusters (although it has since been used in several surveys with short integrations and to process a lot of X-ray data as well). To date, PRESTO has discovered well over a hundred and fifty pulsars, including approximately 100 recycled pulsars, about 80 of which are in binaries. It is written primarily in ANSI C, with many of the recent routines in Python.
Written with portability, ease-of-use, and memory efficiency in mind, it can currently handle raw data from the following pulsar machines or formats:
ProC (short for Process Coordinator) is a versatile workflow engine that allows the user to build, run and manage workflows with just a few clicks. It automatically documents every processing step, making every modification to data reproducible. ProC provides a graphical user interface for constructing complex data processing workflows out of a given set of computer programs. The user can, for example, specify that only data products which are affected by a change in the input data are updated selectively, avoiding unnecessary computations. The ProC suite is flexible and satisfies basic needs of data processing centers that have to be able to restructure their data processing along with the development of a project.
PROFFIT analyzes X-ray surface-brightness profiles for data from any X-ray instrument. It can extract surface-brightness profiles in circular or elliptical annuli, using constant or logarithmic bin size, from the image centroid, the surface-brightness peak, or any user-given center, and provides surface-brightness profiles in any circular or elliptical sectors. It offers background map support to extract background profiles, can excise areas using SAO DS9-compatible (ascl:0003.002) region files to exclude point sources, provides fitting with a number of built-in models, including the popular beta model, double beta, cusp beta, power law, and projected broken power law, uses chi-squared or C statistic, and can fit on the surface-brightness or counts data. It has a command-line interface similar to HEASOFT’s XSPEC (ascl:9910.005) package, provides interactive help with a description of all the commands, and results can be saved in FITS, ROOT or TXT format.
Written in Python, PROFILER analyzes the radial surface brightness profiles of galaxies. It accurately models a wide range of galaxies and galaxy components, such as elliptical galaxies, the bulges of spiral and lenticular galaxies, nuclear sources, discs, bars, rings, and spiral arms with a variety of parametric functions routinely employed in the field (Sérsic, core-Sérsic, exponential, Gaussian, Moffat and Ferrers). In addition, Profiler can employ the broken exponential model (relevant for disc truncations or antitruncations) and two special cases of the edge-on disc model: namely along the major axis (in the disc plane) and along the minor axis (perpendicular to the disc plane).
ProFit is a Bayesian galaxy fitting tool that uses the fast C++ image generation library libprofit (ascl:1612.003) and a flexible R interface to a large number of likelihood samplers. It offers a fully featured Bayesian interface to galaxy model fitting (also called profiling), using mostly the same standard inputs as other popular codes (e.g. GALFIT ascl:1104.010), but it is also able to use complex priors and a number of likelihoods.
The PROFIT is an IDL routine to do automated fitting of emission-line profiles by Gaussian curves or Gauss-Hermite series optimized for use in Integral Field and Fabry-Perot data cubes. As output PROFIT gives two-dimensional FITS files for the emission-line flux distribution, centroid velocity, velocity dispersion and higher order Gauss-Hermite moments (h3 and h4).
PROM4 computes simple models of solar prominences which consist of plane-parallel slabs standing vertically above the solar surface. Each model is defined by 5 parameters: temperature, density, geometrical thickness, microturbulent velocity and height above the solar surface. PROM4 solves the equations of radiative transfer, statistical equilibrium, ionization and pressure equilibria, and computes electron and hydrogen level populations and hydrogen line profiles. Written in Fortran 90 and with two versions available (one with text in English, one with text in French), the code needs 64-bit arithmetic for real numbers.
PromptNuFlux computes the prompt atmospheric neutrino flux E3Φ(GeV2/(cm2ssr)), including the total associated theory uncertainty, for a range of energies between E=103 GeV and E=107.5 GeV. Results are available for five different parametrizations of the input cosmic ray flux: BPL, H3P, H3A, H14a, H14b.
PROPER simulates the propagation of light through an optical system using Fourier transform algorithms (Fresnel, angular spectrum methods). Distributed as IDL source code, it includes routines to create complex apertures, aberrated wavefronts, and deformable mirrors. It is especially useful for the simulation of high contrast imaging telescopes (extrasolar planet imagers like TPF).
PROS is a multi-mission x-ray analysis software system designed to run under IRAF. The PROS software includes spatial, spectral, timing, data I/O and conversion routines, plotting applications, and general algorithms for performing arithmetic operations with imaging data.
PSFEx (“PSF Extractor”) extracts models of the Point Spread Function (PSF) from FITS images processed with SExtractor and measures the quality of images. The generated PSF models can be used for model-fitting photometry or morphological analyses.
The Planck Sky Model (PSM) is a global representation of the multi-component sky at frequencies ranging from a few GHz to a few THz. It summarizes in a synthetic way as much of our present knowledge as possible of the GHz sky. PSM is a complete and versatile set of programs and data that can be used for the simulation or the prediction of sky emission in the frequency range of typical CMB experiments, and in particular of the Planck sky mission. It was originally developed as part of the activities of Planck component separation Working Group (or "Working Group 2" - WG2), and of the ADAMIS team at APC.
PSM gives users the opportunity to investigate the model in some depth: look at its parameters, visualize its predictions for all individual components in various formats, simulate sky emission compatible with a given parameter set, and observe the modeled sky with a synthetic instrument. In particular, it makes possible the simulation of sky emission maps as could be plausibly observed by Planck or other CMB experiments that can be used as inputs for the development and testing of data processing and analysis techniques.
PSOAP (Precision Spectroscopic Orbits A-Parametrically) uses Gaussian processes to infer component spectra of single-lined and double-lined spectroscopic binaries, while simultaneously exploring the posteriors of the orbital parameters and the spectra themselves. PSOAP accounts for the natural λ-covariances in each spectrum, thus providing a natural "de-noising" of the spectra typically offered by Fourier techniques.
PSpectRe, written in C++, uses Fourier-space pseudo-spectral methods to evolve interacting scalar fields in an expanding universe. The code is optimized for the analysis of parametric resonance in the post-inflationary universe and provides an alternative to finite differencing codes. PSpectRe has both second- (Velocity-Verlet) and fourth-order (Runge-Kutta) time integrators. In some circumstances PSpectRe obtains reliable results while using substantially fewer points than a finite differencing code by computing the post-resonance equation of state. PSpectRe is designed to be easily extended to other problems in early-universe cosmology, including the generation of gravitational waves during phase transitions and pre-inflationary bubble collisions.
PSPLINE is a collection of Spline and Hermite interpolation tools for 1D, 2D, and 3D datasets on rectilinear grids. Spline routines give full control over boundary conditions, including periodic, 1st or 2nd derivative match, or divided difference-based boundary conditions on either end of each grid dimension. Hermite routines take the function value and derivatives at each grid point as input, giving back a representation of the function between grid points. Routines are provided for creating Hermite datasets, with appropriate boundary conditions applied. The 1D spline and Hermite routines are based on standard methods; the 2D and 3D spline or Hermite interpolation functions are constructed from 1D spline or Hermite interpolation functions in a straightforward manner. Spline and Hermite interpolation functions are often much faster to evaluate than other representations using e.g. Fourier series or otherwise involving transcendental functions.
PSRCHIVE is an Open Source C++ development library for the analysis of pulsar astronomical data. It implements an extensive range of algorithms for use in pulsar timing, polarimetric calibration, single-pulse analyses, RFI mitigation, scintillation studies, etc. These tools are utilized by a powerful suite of user-end programs that come with the library.
PSRPOP is a package developed to model the Galactic population and evolution of radio pulsars. It is a collection of modules written in Fortran77 for an analysis of a large sample of pulsars detected by the Parkes Multibeam Pulsar Survey. The main programs are:
PsrPopPy is a Python implementation of the Galactic population and evolution of radio pulsars modelling code PSRPOP.
Pulse Portraiture is a wideband pulsar timing code written in python. It uses an extension of the FFTFIT algorithm (Taylor 1992) to simultaneously measure a phase (TOA) and dispersion measure (DM). The code includes a Gaussian-component-based portrait modeling routine. The code uses the python interface to the pulsar data analysis package PSRCHIVE (ascl:1105.014) and also requires the non-linear least-squares minimization package lmfit (ascl:1606.014).
The pS2HAT routines allow efficient, parallel calculation of the so-called 'pure' polarized multipoles. The computed multipole coefficients are equal to the standard pseudo-multipoles calculated for the apodized sky maps of the Stokes parameters Q and U subsequently corrected by so-called counterterms. If the applied apodizations fullfill certain boundary conditions, these multipoles correspond to the pure multipoles. Pure multipoles of one type, i.e., either E or B, are ensured not to contain contributions from the other one, at least to within numerical artifacts. They can be therefore further used in the estimation of the sky power spectra via the pseudo power spectrum technique, which has to however correctly account for the applied apodization on the one hand, and the presence of the counterterms, on the other.
In addition, the package contains the routines permitting calculation of the spin-weighted apodizations, given an input scalar, i.e., spin-0 window. The former are needed to compute the counterterms. It also provides routines for maps and window manipulations. The routines are written in C and based on the S2HAT library, which is used to perform all required spherical harmonic transforms as well as all inter-processor communication. They are therefore parallelized using MPI and follow the distributed-memory computational model. The data distribution patterns, pixelization choices, conventions etc are all as those assumed/allowed by the S2HAT library.
PURIFY is a collection of routines written in C that implements different tools for radio-interferometric imaging including file handling (for both visibilities and fits files), implementation of the measurement operator and set-up of the different optimization problems used for image deconvolution. The code calls the generic Sparse OPTimization (SOPT) package to solve the imaging optimization problems.
Given a path defined in sky coordinates and a spectral cube, pvextractor extracts a slice of the cube along that path and along the spectral axis to produce a position-velocity or position-frequency slice. The path can be defined programmatically in pixel or world coordinates, and can also be drawn interactively using a simple GUI. Pvextractor is the main function, but also includes a few utilities related to header trimming and parsing.
Conservative numerical schemes for general relativistic magnetohydrodynamics (GRMHD) require a method for transforming between "conserved'' variables such as momentum and energy density and "primitive" variables such as rest-mass density, internal energy, and components of the four-velocity. The forward transformation (primitive to conserved) has a closed-form solution, but the inverse transformation (conserved to primitive) requires the solution of a set of five nonlinear equations. This code performs the inversion.
pwkit is a collection of miscellaneous astronomical utilities in Python, with an emphasis on radio astronomy, reading and writing various data formats, and convenient command-line utilities. Utilities include basic astronomical calculations, data visualization tools such as mapping arbitrary data to color scales and tracing contours, and data input and output utilities such as streaming output from other programs.
Py-SPHViewer visualizes and explores N-body + Hydrodynamics simulations. The code interpolates the underlying density field (or any other property) traced by a set of particles, using the Smoothed Particle Hydrodynamics (SPH) interpolation scheme, thus producing not only beautiful but also useful scientific images. Py-SPHViewer enables the user to explore simulated volumes using different projections. Py-SPHViewer also provides a natural way to visualize (in a self-consistent fashion) gas dynamical simulations, which use the same technique to compute the interactions between particles.
Pyaneti is a multi-planet radial velocity and transit fit software. The code uses Markov chain Monte Carlo (MCMC) methods with a Bayesian approach and a parallelized ensemble sampler algorithm in Fortran which makes the code fast. It creates posteriors, correlations, and ready-to-publish plots automatically, and handles circular and eccentric orbits. It is capable of multi-planet fitting and handles stellar limb darkening, systemic velocities for multiple instruments, and short and long cadence data, and offers additional capabilities.
PyBDSF (Python Blob Detector and Source Finder, formerly PyBDSM) decomposes radio interferometry images into sources and makes their properties available for further use. PyBDSF can decompose an image into a set of Gaussians, shapelets, or wavelets as well as calculate spectral indices and polarization properties of sources and measure the psf variation across an image. PyBDSF uses an interactive environment based on CASA (ascl:1107.013); PyBDSF may also be used in Python scripts.
pyBLoCXS is a sophisticated Markov chain Monte Carlo (MCMC) based algorithm designed to carry out Bayesian Low-Count X-ray Spectral (BLoCXS) analysis in the Sherpa environment. The code is a Python extension to Sherpa that explores parameter space at a suspected minimum using a predefined Sherpa model to high-energy X-ray spectral data. pyBLoCXS includes a flexible definition of priors and allows for variations in the calibration information. It can be used to compute posterior predictive p-values for the likelihood ratio test. The pyBLoCXS code has been tested with a number of simple single-component spectral models; it should be used with great care in more complex settings.
PyCloudy is a Python library that handles input and output files of the Cloudy photoionization code (Gary Ferland). It can also generate 3D nebula from various runs of the 1D Cloudy code. pyCloudy allows you to:
pycola is a multithreaded Python/Cython N-body code, implementing the Comoving Lagrangian Acceleration (COLA) method in the temporal and spatial domains, which trades accuracy at small-scales to gain computational speed without sacrificing accuracy at large scales. This is especially useful for cheaply generating large ensembles of accurate mock halo catalogs required to study galaxy clustering and weak lensing. The COLA method achieves its speed by calculating the large-scale dynamics exactly using LPT while letting the N-body code solve for the small scales, without requiring it to capture exactly the internal dynamics of halos.
PyCOOL is a Python + CUDA program that solves the evolution of interacting scalar fields in an expanding universe. PyCOOL uses modern GPUs to solve this evolution and to make the computation much faster. The code includes numerous post-processing functions that provide useful information about the cosmological model, including various spectra and statistics of the fields.
The detection of cosmic ray hits (cosmics) in fiber-fed integral-field spectroscopy (IFS) data of single exposures is a challenging task because of the complex signal recorded by IFS instruments. Existing detection algorithms are commonly found to be unreliable in the case of IFS data, and the optimal parameter settings are usually unknown a priori for a given dataset. The Calar Alto legacy integral field area (CALIFA) survey generates hundreds of IFS datasets for which a reliable and robust detection algorithm for cosmics is required as an important part of the fully automatic CALIFA data reduction pipeline. PyCosmic combines the edge-detection algorithm of L.A.Cosmic with a point-spread function convolution scheme. PyCosmic is the only algorithm that achieves an acceptable detection performance for CALIFA data. Only for strongly undersampled IFS data does L.A.Cosmic exceed the performance of PyCosmic by a few percent. Thus, PyCosmic appears to be the most versatile cosmics detection algorithm for IFS data.
PyCS is a software toolbox to estimate time delays between multiple images of strongly lensed quasars, from resolved light curves such as obtained by the COSMOGRAIL monitoring program. The pycs package defines a collection of classes and high level functions, that you can script in a flexible way. PyCS makes it easy to compare different point estimators (including your own) without much code integration. The package heavily depends on numpy, scipy, and matplotlib.
PyDrizzle provides a semi-automated interface for computing the parameters necessary for running Drizzle. PyDrizzle performs the task of determining the parameters necessary for aligning images based on the WCS information in the input image headers, as well as any supplemental alignment information provided in shift files, and combines the images onto the same WCS. Though it does not identify cosmic rays, it has the ability to ignore pixels flagged as bad, such as pixels identified by other programs as affected by cosmic rays.
PyEphem provides scientific-grade astronomical computations for the Python programming language. Given a date and location on the Earth’s surface, it can compute the positions of the Sun and Moon, of the planets and their moons, and of any asteroids, comets, or earth satellites whose orbital elements the user can provide. Additional functions are provided to compute the angular separation between two objects in the sky, to determine the constellation in which an object lies, and to find the times at which an object rises, transits, and sets on a particular day.
The numerical routines that lie behind PyEphem are those from the wonderful XEphem astronomy application, whose author, Elwood Downey, generously gave permission for us to use them as the basis for PyEphem.
PYESSENCE evolves linearly perturbed coupled quintessence models with multiple (cold dark matter) CDM fluid species and multiple DE (dark energy) scalar fields, and can be used to generate quantities such as the growth factor of large scale structure for any coupled quintessence model with an arbitrary number of fields and fluids and arbitrary couplings.
The Python script/package pyExtinction computes and plots total atmospheric extinction from decomposition into physical components (Rayleigh attenuation, ozone absorption, aerosol extinction). Its default extinction parameters are adapted to mean Mauna Kea summit conditions.
PyFITS provides an interface to FITS formatted files in the Python scripting language and PyRAF, the Python-based interface to IRAF. It is useful both for interactive data analysis and for writing analysis scripts in Python using FITS files as either input or output. PyFITS is a development project of the Science Software Branch at the Space Telescope Science Institute.
Pyflation calculates cosmological perturbations during an inflationary expansion of the universe. The modules in the pyflation Python package can be used to run simulations of different scalar field models of the early universe. The main classes are contained in the cosmomodels module and include simulations of background fields and first order and second order perturbations. The sourceterm package contains modules required for the computation of the term required for the evolution of second order perturbations.
Alongside the Python package, the bin directory contains Python scripts which can run first and second order simulations. A helper script called pyflation-qsubstart.py sets up a full second order run (including background, first order and source calculations) to be used on queueing system which contains the qsub executable (e.g. a Rocks cluster).
pygad provides a framework for dealing with Gadget snapshots. The code reads any of the many different Gadget (ascl:0003.001) formats, allows easy masking snapshots to particles of interest, decorates the data blocks with units, allows to add automatically updating derived blocks, and provides several binning and plotting routines, among other tasks, to provide convenient, intuitive handling of the Gadget data without the need to worry about technical details. pygad provides access to single stellar population (SSP) models, has an interface to Rockstar (ascl:1210.008) output files, provides its own friends-of-friends (FoF) finder, calculates spherical overdensities, and has a sub-module to generate mock absorption lines.
PyGFit measures PSF-matched photometry from images with disparate pixel scales and PSF sizes; its primary purpose is to extract robust spectral energy distributions (SEDs) from crowded images. It fits blended sources in crowded, low resolution images with models generated from a higher resolution image, thus minimizing the impact of crowding and also yielding consistently measured fluxes in different filters which minimizes systematic uncertainty in the final SEDs.
pyGMMis is a mixtures-of-Gaussians density estimation method that accounts for arbitrary incompleteness in the process that creates the samples as long as the incompleteness is known over the entire feature space and does not depend on the sample density (missing at random). pyGMMis uses the Expectation-Maximization procedure and generates its best guess of the unobserved samples on the fly. It can also incorporate an uniform "background" distribution as well as independent multivariate normal measurement errors for each of the observed samples, and then recovers an estimate of the error-free distribution from which both observed and unobserved samples are drawn. The code automatically segments the data into localized neighborhoods, and is capable of performing density estimation with millions of samples and thousands of model components on machines with sufficient memory.
PyGSM is a Python interface for the Global Sky Model (GSM, ascl:1011.010). The GSM is a model of diffuse galactic radio emission, constructed from a variety of all-sky surveys spanning the radio band (e.g. Haslam and WMAP). PyGSM uses the GSM to generate all-sky maps in Healpix format of diffuse Galactic radio emission from 10 MHz to 94 GHz. The PyGSM module provides visualization utilities, file output in FITS format, and the ability to generate observed skies for a given location and date. PyGSM requires Healpy, PyEphem (ascl:1112.014), and AstroPy (ascl:1304.002).
The pyhrs package reduces data from the High Resolution Spectrograph (HRS) on the Southern African Large Telescope (SALT). HRS is a dual-beam, fiber fed echelle spectrectrograph with four modes of operation: low (R~16000), medium (R~34000), high (R~65000), and high stability (R~65000). pyhrs, written in Python, includes all of the steps necessary to reduce HRS low, medium, and high resolution data; this includes basic CCD reductions, order identification, wavelength calibration, and extraction of the spectra.
PyKE is a python-based PyRAF package that can also be run as a stand-alone program within a unix-based shell without compiling against PyRAF. It is a group of tasks developed for the reduction and analysis of Kepler Simple Aperture Photometry (SAP) data of individual targets with individual characteristics. The main purposes of these tasks are to i) re-extract light curves from manually-chosen pixel apertures and ii) cotrend and/or detrend the data in order to reduce or remove systematic noise structure using methods tunable to user and target-specific requirements. PyKE is an open source project and contributions of new tasks or enhanced functionality of existing tasks by the community are welcome.
pyKLIP subtracts out the stellar PSF to search for directly-imaged exoplanets and disks using a Python implementation of the Karhunen-Loève Image Projection (KLIP) algorithm. pyKLIP supports ADI, SDI, and ADI+SDI to model the stellar PSF and offers a large array of PSF subtraction parameters to optimize the reduction. pyKLIP relies on a minimal amount of dependencies (numpy, scipy, and astropy) and parallelizes the KLIP algorithm to speed up the reduction. pyKLIP supports GPI and P1640 data and can interface with other data sources with the addition of new modules. It also can inject simulated planets and disks as well as automatically search for point sources in PSF-subtracted data.
pyLCSIM simulates X-ray lightcurves from coherent signals and power spectrum models. Coherent signals can be specified as a sum of one or more sinusoids, each with its frequency, pulsed fraction and phase shift; or as a series of harmonics of a fundamental frequency (each with its pulsed fraction and phase shift). Power spectra can be simulated from a model of the power spectrum density (PSD) using as a template one or more of the built-in library functions. The user can also define his/her custom models. Models are additive.
PyLDTk automates the calculation of custom stellar limb darkening (LD) profiles and model-specific limb darkening coefficients (LDC) using the library of PHOENIX-generated specific intensity spectra by Husser et al. (2013). It facilitates exoplanet transit light curve modeling, especially transmission spectroscopy where the modeling is carried out for custom narrow passbands. PyLDTk construct model-specific priors on the limb darkening coefficients prior to the transit light curve modeling. It can also be directly integrated into the log posterior computation of any pre-existing transit modeling code with minimal modifications to constrain the LD model parameter space directly by the LD profile, allowing for the marginalization over the whole parameter space that can explain the profile without the need to approximate this constraint by a prior distribution. This is useful when using a high-order limb darkening model where the coefficients are often correlated, and the priors estimated from the tabulated values usually fail to include these correlations.
pylightcurve is a model for light-curves of transiting planets. It uses the four coefficients law for the stellar limb darkening and returns the relative flux, F(t), as a function of the limb darkening coefficients, an, the Rp/R* ratio and all the orbital parameters based on the nonlinear limb darkening model (Claret 2000).
pyLIMA is an open source software for microlensing modeling. Based on Python, the goal is to offer to users an efficient and user friendly package to analyze their data. The code is written and tested with professional standards, such as PEP8 or unit testing.
PyMC is a python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo. Its flexibility and extensibility make it applicable to a large suite of problems. Along with core sampling functionality, PyMC includes methods for summarizing output, plotting, goodness-of-fit and convergence diagnostics.
PyMC3 performs Bayesian statistical modeling and model fitting focused on advanced Markov chain Monte Carlo and variational fitting algorithms. It offers powerful sampling algorithms, such as the No U-Turn Sampler, allowing complex models with thousands of parameters with little specialized knowledge of fitting algorithms, intuitive model specification syntax, and optimization for finding the maximum a posteriori (MAP) point. PyMC3 uses Theano to compute gradients via automatic differentiation as well as compile probabilistic programs on-the-fly to C for increased speed.
pyMCZ calculates metallicity according to a number of strong line metallicity diagnostics from spectroscopy line measurements and obtain uncertainties from the line flux errors in a Monte Carlo framework. Given line flux measurements and their uncertainties, pyMCZ produces synthetic distributions for the oxygen abundance in up to 13 metallicity scales simultaneously, as well as for E(B-V), and estimates their median values and their 68% confidence regions. The code can output the full MC distributions and their kernel density estimates.
PyMGC3 is a Python toolkit to apply the Modified Great Circle Cell Counts (mGC3) method to search for tidal streams in the Galactic Halo. The code computes pole count maps using the full mGC3/nGC3/GC3 family of methods. The original GC3 method (Johnston et al., 1996) uses positional information to search for 'great-circle-cell structures'; mGC3 makes use of full 6D data and nGC3 uses positional and proper motion data.
PyMidas is an interface between Python and MIDAS, the major ESO legacy general purpose data processing system. PyMidas allows a user to exploit both the rich legacy of MIDAS software and the power of Python scripting in a unified interactive environment. PyMidas also allows the usage of other Python-based astronomical analysis systems such as PyRAF.
PyMOC manipulates Multi-Order Coverage (MOC) maps. It supports reading and writing the three encodings mentioned in the IVOA MOC recommendation: FITS, JSON and ASCII.
PyModelFit provides a pythonic, object-oriented framework that simplifies the task of designing numerical models to fit data. This is a very broad task, and hence the current functionality of PyModelFit focuses on the simpler tasks of 1D curve-fitting, including a GUI interface to simplify interactive work (using Enthought Traits). For more complicated modeling, PyModelFit also provides a wide range of classes and a framework to support more general model/data types (2D to Scalar, 3D to Scalar, 3D to 3D, and so on).
PyMSES provides a python solution for getting data out of RAMSES (ascl:1011.007) astrophysical fluid dynamics simulations. It permits transparent manipulation of large simulations and interfaces with common Python libraries and existing code, and can serve as a post-processing toolbox for data analysis. It also does three-dimensional volume rendering with a specific algorithm optimized to work on RAMSES distributed data (Guillet et al. 2011 and Jones et a. 2011).
PyMultiNest provides programmatic access to MultiNest (ascl:1109.006) and PyCuba, integration existing Python code (numpy, scipy), and enables writing Prior & LogLikelihood functions in Python. PyMultiNest can plot and visualize MultiNest's progress and allows easy plotting, visualization and summarization of MultiNest results. The plotting can be run on existing MultiNest output, and when not using PyMultiNest for running MultiNest.
PyMVPA eases statistical learning analyses of large datasets. It offers an extensible framework with a high-level interface to a broad range of algorithms for classification, regression, feature selection, data import and export. It is designed to integrate well with related software packages, such as scikit-learn, shogun, and MDP.
Pynbody is a lightweight, portable, format-transparent analysis package for astrophysical N-body and smooth particle hydrodynamic simulations supporting PKDGRAV/Gasoline, Gadget, N-Chilada, and RAMSES AMR outputs. Written in python, the core tools are accompanied by a library of publication-level analysis routines.
PyNeb (previously PyNebular) is an update and expansion of the IRAF package NEBULAR; rewritten in Python, it is designed to be more user-friendly and powerful, increasing the speed, easiness of use, and graphic visualization of emission lines analysis. In PyNeb, the atom is represented as an n-level atom. For given density and temperature, PyNeb solves the equilibrium equations and determines the level populations. PyNeb can compute physical conditions from suitable diagnostic line ratios and level populations, critical densities and line emissivities, and can compute and display emissivity grids as a function of Te and Ne. It can also deredden line intensities, read and manage observational data, and plot and compare atomic data from different publications, and compute ionic abundances from line intensities and physical conditions and elemental abundances from ionic abundances and icfs.
PynPoint uses principal component analysis to detect and estimate the flux of exoplanets in two-dimensional imaging data. It processes many, typically several thousands, of frames to remove the light from the star so as to reveal the companion planet.
PyORBIT handles several kinds of datasets, such as radial velocity (RV), activity indexes, and photometry, to simultaneously characterize the orbital parameters of exoplanets and the noise induced by the activity of the host star. RV computation is performed using either non-interacting Kepler orbits or n-body integration. Stellar activity can be modeled either with sinusoids at the rotational period and its harmonics or Gaussian process. In addition, the code can model offsets and systematics in measurements from several instruments. The PyORBIT code is modular; new methods for stellar activity modeling or parameter estimation can easily be incorporated into the code.
PyPHER (Python-based PSF Homogenization kERnels) computes an homogenization kernel between two PSFs; the code is well-suited for PSF matching applications in both an astronomical or microscopy context. It can warp (rotation + resampling) the PSF images (if necessary), filter images in Fourier space using a regularized Wiener filter, and produce a homogenization kernel. PyPHER requires the pixel scale information to be present in the FITS files, which can if necessary be added by using the provided ADDPIXSCL method.
pyprofit is a python wrapper for libprofit (ascl:1612.003).
PyPulse handles PSRFITS files and performs subsequent analyses on pulse profiles.
pyraf-dbsp is a PyRAF-based (ascl:1207.011) reduction pipeline for optical spectra taken with the Palomar 200-inch Double Beam Spectrograph. The pipeline provides a simplified interface for basic reduction of single-object spectra with minimal overhead. It is suitable for quicklook classification of transients as well as moderate-precision (few km/s) radial velocity work.
PyRAF is a command language for running IRAF tasks that is based on the Python scripting language. It gives users the ability to run IRAF tasks in an environment that has all the power and flexibility of Python. PyRAF can be installed along with an existing IRAF installation; users can then choose to run either PyRAF or the IRAF CL.
A python interface to the JINA reaclib nuclear reaction database
pyro is a simple python-based tutorial on computational methods for hydrodynamics. It includes 2-d solvers for advection, compressible, incompressible, and low Mach number hydrodynamics, diffusion, and multigrid. It is written with ease of understanding in mind. An extensive set of notes that is part of the Open Astrophysics Bookshelf project provides details of the algorithms.
The PySALT user package contains the primary reduction and analysis software tools for the SALT telescope. Currently, these tools include basic data reductions for RSS and SALTICAM in both imaging, spectroscopic, and slot modes. Basic analysis software for slot mode data is also provided. These tools are primarily written in python/PyRAF with some additional IRAF code.
PySM generates full-sky simulations of Galactic foregrounds in intensity and polarization relevant for CMB experiments. The components simulated are thermal dust, synchrotron, AME, free-free, and CMB at a given Nside, with an option to integrate over a top hat bandpass, to add white instrument noise, and to smooth with a given beam. PySM is based on the large-scale Galactic part of Planck Sky Model code and uses some of its inputs
The pYSOVAR code calculates properties for a stack of lightcurves, including simple descriptive statistics (mean, max, min, ...), timing (e.g. Lomb-Scargle periodograms), variability indixes (e.g. Stetson), and color properties (e.g. slope in the color-magnitude diagram). The code is written in python and is closely integrated with astropy tables. Initially, pYSOVAR was written specifically for the analysis of two clusters in the YSOVAR project, using the (not publicly released) YSOVAR database as an input. Additional functionality has been added and the code has become more general; it is now useful for other clusters in the YSOVAR dataset or for other projects that have similar data (lightcurves in one or more bands with a few hundred points for a few thousand objects), though may not work out-of-the-box for different datasets.
pysovo contains basic tools to work with VOEvents. Though written for specific needs, others interested in VOEvents may find it useful to examine.
PySpecKit is a Python spectroscopic analysis and reduction toolkit meant to be generally applicable to optical, infrared, and radio spectra. It is capable of reading FITS-standard and many non-standard file types including CLASS spectra. It contains procedures for line fitting including gaussian and voigt profile fitters, and baseline-subtraction routines. It is capable of more advanced line fitting using arbitrary model grids. Fitting can be done both in batch mode and interactively. PySpecKit also produces publication-quality plots with TeX axis labels and annotations. It is designed to be extensible, allowing user-written reader, writer, and fitting routines to be "plugged in." It is actively under development and currently in the 'alpha' phase, with plans for a beta release.
pysynphot is a synthetic photometry software package suitable for either library or interactive use. Intended as a modern-language successor to the IRAF/STSDAS synphot package, it provides improved algorithms that address known shortcomings in synphot, and its object-oriented design is more easily extensible than synphot's task-oriented approach. It runs under PyRAF, and a backwards compatibility mode is provided that recognizes all spectral and throughput tables, obsmodes, and spectral expressions used by synphot, to facilitate the transition for legacy code.
This code is meant to emulate a fortran program written by B. Peterson for use with reverberation mapping.
The code cross correlates two light curves that are unevenly sampled using linear interpolation and measures the peak and centroid of the cross-correlation function. In addition, it is possible to run Monteo Carlo iterations using flux randomization and random subset selection (RSS) to produce cross-correlation centroid distributions to estimate the uncertainties in the cross correlation results. The ideas behind the methodology that this code implements are described in detail by Peterson et al. (1998): http://arxiv.org/abs/astro-ph/9802103
Python-CPL is a framework to configure and execute pipeline recipes written with the Common Pipeline Library (CPL) (ascl:1402.010) with Python2 or Python3. The input, calibration and output data can be specified as FITS files or as astropy.io.fits objects in memory. The package is used to implement the MUSE pipeline in the AstroWISE data management system.
Characterization of the frequency response of coherent radiometric receivers is a key element in estimating the flux of astrophysical emissions, since the measured signal depends on the convolution of the source spectral emission with the instrument band shape. Python-qucs automates the process of preparing input data, running simulations and exporting results of QUCS (Quasi Universal Circuit Simulator) simulations.
PythonPhot is a simple Python translation of DAOPHOT-type (ascl:1104.011) photometry procedures from the IDL AstroLib (Landsman 1993), including aperture and PSF-fitting algorithms, with a few modest additions to increase functionality and ease of use. These codes allow fast, easy, and reliable photometric measurements and are currently used in the Pan-STARRS supernova pipeline and the HST CLASH/CANDELS supernova analysis.
PyTransit implements optimized versions of the Giménez and Mandel & Agol transit models for exoplanet transit light-curves. The two models are implemented natively in Fortran with OpenMP parallelization, and are accessed by an object-oriented python interface. PyTransit facilitates the analysis of photometric time series of exoplanet transits consisting of hundreds of thousands of data points, and of multipassband transit light curves from spectrophotometric observations. It offers efficient model evaluation for multicolour observations and transmission spectroscopy, built-in supersampling to account for extended exposure times, and routines to calculate the projected planet-to-star distance for circular and eccentric orbits, transit durations, and more.
PyTransport calculates the 2-point and 3-point function of inflationary perturbations produced during multi-field inflation. The core of PyTransport is C++ code which is automatically edited and compiled into a Python module once an inflationary potential is specified. This module can then be called to solve the background inflationary cosmology as well as the evolution of correlations of inflationary perturbations. PyTransport includes two additional modules written in Python, one to perform the editing and compilation, and one containing a suite of functions for common tasks such as looping over the core module to construct spectra and bispectra.
PyVO provides access to remote data and services of the Virtual observatory (VO) using Python. It allows archive searches for data of a particular type or related to a particular topic and query submissions to obtain data to a particular archive to download selected data products. PyVO supports querying the VAO registry; simple data access services (DAL) to access images (SIA), source catalog records (Cone Search), spectra (SSA), and spectral line emission/absorption data (SLAP); and object name resolution (for converting names of objects in the sky into positions). PyVO requires both AstroPy and NumPy.
PyWiFeS is a Python-based data reduction pipeline for the Wide Field Spectrograph (WiFeS). Its core data processing routines are built on standard scientific Python packages commonly used in astronomical applications. It includes an implementation of a global optical model of the spectrograph which provides wavelengths solutions accurate to ˜0.05 Å (RMS) across the entire detector. Through scripting, PyWiFeS can enable batch processing of large quantities of data.
pyXSIM simulates X-ray observations from astrophysical sources. X-rays probe the high-energy universe, from hot galaxy clusters to compact objects such as neutron stars and black holes and many interesting sources in between. pyXSIM generates synthetic X-ray observations of these sources from a wide variety of models, whether from grid-based simulation codes such as FLASH (ascl:1010.082), Enzo (ascl:1010.072), and Athena (ascl:1010.014), to particle-based codes such as Gadget (ascl:0003.001) and AREPO, and even from datasets that have been created “by hand”, such as from NumPy arrays. pyXSIM can also manipulate the synthetic observations it produces in various ways and export the simulated X-ray events to other software packages to simulate the end products of specific X-ray observatories. pyXSIM is an implementation of the PHOX (ascl:1112.004) algorithm and was initially the photon_simulator analysis module in yt (ascl:1011.022); it is dependent on yt.
QATS detects transiting extrasolar planets in time-series photometry. It relaxes the usual assumption of strictly periodic transits by permitting a variable, but bounded, interval between successive transits.
QDPHOT is a fast CCD stellar photometry task which quickly produces CCD stellar photometry from two CCD images of a star field. It was designed to be a data mining tool for finding high-quality stellar observations in the data archives of the National Virtual Observatory. QDPHOT typically takes just a few seconds to analyze two Hubble Space Telescope WFPC2 observations of Local Group star clusters. It is also suitable for real-time data-quality analysis of CCD observations; on-the-fly instrumental color-magnitude diagrams can be produced at the telescope console during the few seconds between CCD readouts.
QFitsView is a FITS file viewer that can display one, two, and three-dimensional FITS files. It has three modes of operation, depending of what kind of data is being displayed. One-dimensional data are shown in an x-y plot. Two-dimensional images are shown in the main window. Three-dimensional data cubes can be displayed in a variety of ways, with the third dimension shown as a x-y plot at the bottom of the image display. QFitsView was written in C++ and uses the Qt widget library, which makes it available for all major platforms: Windows, MAC OS X, and many Unix variants.
Qhull computes the convex hull, Delaunay triangulation, Voronoi diagram, halfspace intersection about a point, furthest-site Delaunay triangulation, and furthest-site Voronoi diagram. The source code runs in 2-d, 3-d, 4-d, and higher dimensions. Qhull implements the Quickhull algorithm for computing the convex hull. It handles roundoff errors from floating point arithmetic. It computes volumes, surface areas, and approximations to the convex hull.
QSFit performs automatic analysis of Active Galactic Nuclei (AGN) optical spectra. It provides estimates of: AGN continuum luminosities and slopes at several restframe wavelengths; luminosities, widths and velocity offsets of 20 emission lines; luminosities of iron blended lines at optical and UV wavelengths; host galaxy luminosities. The whole fitting process is customizable for specific needs, and can be extended to analyze spectra from other data sources. The ultimate purpose of QSFit is to allow astronomers to run standardized recipes to analyze the AGN data, in a simple, replicable and shareable way.
QtClassify is a GUI that helps classify emission lines found in integral field spectroscopic data. Input needed is a datacube as well as a catalog with emission lines and a signal-to-noise cube, such at that created by LSDCat (ascl:1612.002). The main idea is to take each detected line and guess what line it could be (and thus the redshift of the object). You would expect to see other lines that might not have been detected but are visible in the cube if you know where to look, which is why parts of the spectrum are shown where other lines are expected. In addition, monochromatic layers of the datacube are displayed, making it easy to spot additional emission lines.
Quickclump finds clumps in a 3D FITS datacube. It is a fast, accurate, and automated tool written in Python. Though Quickclump is primarily intended for decomposing observations of interstellar clouds into individual clumps, it can also be used for finding clumps in any 3D rectangular data.
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