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).

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

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.

PyMUSE analyzes VLT/MUSE datacubes. The package is optimized to extract 1-D spectra of arbitrary spatial regions within the cube and also for producing images using photometric filters and customized masks. It is intended to provide the user the tools required for a complete analysis of a MUSE data set.

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.

PyNAPLE (PYthon Nac Automated Pair Lunar Evaluator) detects changes and new impact craters on the lunar surface using Lunar Reconnaissance Orbiter Narrow Angle Camera (LRO NAC) images. The code enables large scale analyses of sub-kilometer scale cratering rates and refinement of both scaling laws and the luminous efficiency.

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.

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

PynPoint processes and analyzes high-contrast imaging data of exoplanets and circumstellar disks. The generic, end-to-end pipeline's modular architecture separates the core functionalities and the pipeline modules. These modules have specific tasks such as background subtraction, frame selection, centering, PSF subtraction with principal component analysis, estimation of detection limits, and photometric and astrometric analysis. All modules store their results in a central database. Management of the available hardware by the backend of the pipeline is in particular an advantage for data sets containing thousands of images, as is common in the mid-infrared wavelength regime. This version of PynPoint is a significant rewrite of the earlier PynPoint package (ascl:1501.001).

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.

The code has been significantly rewritten and expanded; please see ascl:1812.010.

pynucastro interfaces to the nuclear reaction rate databases, including the JINA Reaclib nuclear reactions database. This set of Python interfaces enables interactive exploration of rates and collection of rates (networks) in Jupyter notebooks and easy creation of the righthand side routines for reaction network integration (the ODEs) for use in simulation codes.

pyobs enables remote and fully autonomous observation control of astronomical telescopes. It provides an abstraction layer over existing drivers and a means of communication between different devices (called modules in pyobs). The code can also act as a hardware driver for all the devices used at an observatory. In addition, pyobs offers non-hardware-related modules for automating focusing, acquisition, guiding, and other routine tasks.

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.

PyOSE is a fully numerical orbital sampling effect (OSE) simulator that can model arbitrary inclinations of the transiting moon orbit. It can be used to search for exomoons in long-term stellar light curves such as those by Kepler and the upcoming PLATO mission.

PyPDR calculates the chemistry, thermal balance and molecular excitation of a slab of gas under FUV irradiation in a self-consistent way. The effect of FUV irradiation on the chemistry is that molecules get photodissociated and the gas is heated up to several 1000 K, mostly by the photoelectric effect on small dust grains or UV pumping of H2 followed by collision de-excitation. The gas is cooled by molecular and atomic lines, thus indirectly the chemical composition also affects the thermal structure through the abundance of molecules and atoms. To find a self-consistent solution between heating and cooling, the code iteratively calculates the chemistry, thermal-balance and molecular/atomic excitation.

We present a module built into the PypeIt Python package to reduce high resolution Y, J, H, K, and L band spectra from the W. M. Keck Observatory NIRSPEC spectrograph. This data reduction pipeline is capable of spectral extraction, wavelength calibration, and telluric correction of data taken before and after the 2018 detector upgrade, all in a single package. The procedure for reducing data is thoroughly documented in an expansive tutorial.

PypeIt reduces data from echelle and low-resolution spectrometers; the code can be run in several modes of reduction that demark the level of sophistication (e.g. quick and dirty vs. MonteCarlo) and also the amount of output written to disk. It also generates numerous data products, including 1D and 2D spectra; calibration images, fits, and meta files; and quality assurance figures.

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

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.

PyPion reads in Silo (ascl:2103.025) data files from PION (ascl:2103.024) simulations and plots the data. This library works for 1D, 2D, and 3D data files and for any amount of nested-grid levels. The scripts contained in PyPion save the options entered into the command line when the python script is run, open the silo file and save all of the important header variables, open the directory in the silo (or vtk, or fits) file and save the requested variable data (eg. density, temp, etc.), and set up the plotting function and the figure.

The spectroscopy analysis pipeline pyPipe3D produces coherent and easy to distribute and compare parameters of stellar populations and ionized gas; it is suited in particular for data from the most recent optical IFS surveys. The pipeline is build using pyFIT3D, which is the main spectral fitting module included in this package.

pyPplusS calculates the light curves for ringed, oblate or spherical exoplanets in both the uniform and limb darkened cases. It can constrain the oblateness of planets using photometric data only. This code can be used to model light curves of more complicated configurations, including multiple planets, oblate planets, moons, rings, and combinations of these, while properly and efficiently taking into account overlapping areas and limb darkening.

pyprofit is a python wrapper for libprofit (ascl:1612.003).

PyPulse handles PSRFITS files and performs subsequent analyses on pulse profiles.

The Python QSO fitting code (PyQSOFit) measures spectral properties of quasars. Based on Shen's IDL version, this code decomposes different components in the quasar spectrum, e.g., host galaxy, power-law continuum, Fe II component, and emission lines. In addition, it can run Monto Carlo iterations using flux randomization to estimate the uncertainties.

pyqz computes the values of log(Q) [the ionization parameter] and 12+log(O/H) [the oxygen abundance, either total or in the gas phase] for a given set of strong emission lines fluxes from HII regions. The log(Q) and 12+log(O/H) values are interpolated from a finite set of diagnostic line ratio grids computed with the MAPPINGS V code (ascl:1807.005). The grids used by pyqz are chosen to be flat, without wraps, to decouple the influence of log(Q) and 12+log(O/H) on the emission line ratios.

The 1D radiation code PyRADS provides line-by-line spectral resolution. For Earth-like atmospheres, PyRADS currently uses HITRAN 2016 line lists and the MTCKD continuum model. A version for shortwave radiation (scattering) is also available.

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.

Pyrat Bay computes radiative-transfer spectra and fits exoplanet atmospheric properties, and is an efficient, user-friendly Python tool. The package offers transmission or emission spectra of exoplanet transit or eclipses respectively and forward-model or retrieval calculations. The radiative-transfer includes opacity sources from line-by-line molecular absorption, collision-induced absorption, Rayleigh scattering absorption, and more, including Gray aerosol opacities. Pyrat Bay's Bayesian (MCMC) posterior sampling of atmospheric parameters includes molecular abundances, temperature profile, pressure-radius, and Rayleigh and cloud properties.

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

A python interface to the JINA reaclib nuclear reaction database

Pyriod provides basic period detection and fitting routines for astronomical time series. Written in Python and designed to be run interactively in a Jupyter notebook, it displays and allows the user to interact with time series data, fit frequency solutions, and save figures from the toolbar. It can display original or residuals time series, fold the time series on some frequency, add selected peaks from the periodogram to the model, and refine the fit by computing a least-squared fit of the model using Lmfit (ascl:1606.014).

Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch. It can represent any computable probability distribution and scales to large data sets with little overhead compared to hand-written code. The library is implemented with a small core of powerful, composable abstractions. Its high-level abstractions express generative and inference models, but also allows experts to customize inference.

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.

PyROA models quasar light curves where the variability is described using a running optimal average (ROA), and parameters are sampled using Markov Chain Monte Carlo (MCMC) techniques using emcee (ascl:1303.002). Using a Bayesian approach, priors can be used on the sampled parameters. Currently it has three main uses: 1.) Determining the time delay between lightcurves at different wavelengths; 2.) Intercalibrating light curves from multiple telescopes, merging them into a single lightcurve; and 3.) Determining the time delay between images of lensed quasars, where the microlensing effects are also modeled. PyROA also includes a noise model, where there is a parameter for each light curve that adds extra variance to the flux measurments, to account for underestimated errors; this can be turned off if required. Example jupyter notebooks that demonstrate each of the three main uses of the code are provided.

pyRRG measures the 2nd and 4th order moments using a TinyTim model to correct for PSF distortions. The code is invariant to the number exposures and orientation of the drizzle images. pyRRG uses a machine learning algorithm to automatically classify stars and galaxies; this can also be done manually if greater accuracy is needed.

pyRSD computes the theoretical predictions of the redshift-space power spectrum of galaxies. It also includes functionality for fitting data measurements and finding the optimal model parameters, using both MCMC and nonlinear optimization techniques.

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.

PySAP (Python Sparse data Analysis Package) provides a common API for astronomical and neuroimaging datasets and access to iSAP's (ascl:1303.029) Sparse2D executables with both wrappers and bindings. It also offers a graphical user interface for exploring the provided functions and access to application specific plugins.

The Python Satellite Data Analysis Toolkit (pysat) provides a simple and flexible interface for downloading, loading, cleaning, managing, processing, and analyzing space science data. The toolkit supports in situ satellite observations and many different types of ground- and space-based measurements. Its analysis routines are independent of instrument and data source.

pySDR performs local gradient clustering-based sharpened dimensionality reduction (SDR). The library uses the C++ LGCDR_v1 code as its backend.

PySE finds and measures sources in radio telescope images. It is run with several options, such as the detection threshold (a multiple of the local noise), grid size, and the forced clean beam fit, followed by a list of input image files in standard FITS or CASA format. From these, PySe provides a list of found sources; information such as the calculated background image, source list in different formats (e.g. text, region files importable in DS9), and other data may be saved. PySe can be integrated into a pipeline; it was originally written as part of the LOFAR Transient Detection Pipeline (TraP, ascl:1412.011).

Pyshellspec models binary systems with circumstellar matter (e.g. accretion disk, jet, shell), computes the interferometric observables |V2|, arg T3, |T3|, |dV|, and arg dV, and performs comparisons of light curves, spectro-interferometry, spectra, and SED with observations, and both global and local optimization of system parameters. The code solves the inverse problem of finding the stellar and orbital parameters of the stars and circumstellar medium. Pyshellspec is based on the long-characteristic LTE radiation transfer code Shellspec (ascl:1108.017).

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

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.

PySME is a partial reimplementation of Spectroscopy Made Easy (SME, ascl:1202.013), which fits an observed spectrum of a star with a model spectrum. The IDL routines of SME used to call a dynamically linked library of compiled C++ and Fortran programs have been rewritten in Python. In addition, an object oriented paradigm and continuous integration practices, including build automation, self-testing, and frequent builds, have been added.

PYSOLATOR removes the orbital modulation from a binary pulsar and/or its companion. In essence, it subtracts the predicted Roemer delay for the given orbit and then resamples the time series so as to make the signal appear as if it were emitted from the barycenter of the binary system, making the search for pulses easier and faster.

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.