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[ascl:1703.011] QtClassify: IFS data emission line candidates classifier

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.

[ascl:1612.011] QSFit: Quasar Spectral FITting

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.

[ascl:1809.011] qp: Quantile parametrization for probability distribution functions

qp manipulates parametrizations of 1-dimensional probability distribution functions, as suitable for photo-z PDF compression. The code helps determine a parameterization for storing a catalog of photo-z PDFs that balances the available storage resources against the accuracy of the photo-z PDFs and science products reconstructed from the stored parameters.

[ascl:1908.020] QLF: Luminosity function analysis code

QLF derives full posterior distributions for and analyzes luminosity functions models; it also models hydrogen and helium reionization. Used with the included homogenized data, the derived luminosity functions can be easily compared with theoretical models or future data sets.

[ascl:1304.016] Qhull: Quickhull algorithm for computing the convex hull

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.

[ascl:1210.019] QFitsView: FITS file viewer

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.

[ascl:1806.006] QE: Quantum opEn-Source Package for Research in Electronic Structure, Simulation, and Optimization

Quantum ESPRESSO (opEn-Source Package for Research in Electronic Structure, Simulation, and Optimization) is an integrated suite of codes for electronic-structure calculations and materials modeling at the nanoscale. It is based on density-functional theory, plane waves, and pseudopotentials. QE performs ground-state calculations such as self-consistent total energies, forces, stresses and Kohn-Sham orbitals, Car-Parrinello and Born-Oppenheimer molecular dynamics, and quantum transport such as ballistic transport, coherent transport from maximally localized Wannier functions, and Kubo-Greenwood electrical conductivity. It can also determine spectroscopic properties and examine time-dependent density functional perturbations and electronic excitations, and has a wide range of other functions.

[ascl:1601.015] QDPHOT: Quick & Dirty PHOTometry

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.

[ascl:1712.014] QATS: Quasiperiodic Automated Transit Search

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.

[ascl:1908.001] QAC: Quick Array Combinations front end to CASA

QAC (Quick Array Combinations) is a front end to CASA (ascl:1107.013) and calls tools and tasks to help in combining data from a single dish and interferometer. QAC hides some of the complexity of writing CASA scripts and provide a simple interface to array combination tools and tasks in CASA. This project was conceived alongside the TP2VIS (ascl:1904.021) project, where it was used to provide an easier way to call CASA and perform regression tests.

[ascl:1905.008] Q3C: A PostgreSQL package for spatial queries and cross-matches of large astronomical catalogs

Q3C (Quad Tree Cube) enables fast cone, ellipse and polygonal searches and cross-matches between large astronomical catalogs inside a PostgreSQL database. The package supports searches even if objects have proper motions.

[ascl:1806.003] pyZELDA: Python code for Zernike wavefront sensors

pyZELDA analyzes data from Zernike wavefront sensors dedicated to high-contrast imaging applications. This modular software was originally designed to analyze data from the ZELDA wavefront sensor prototype installed in VLT/SPHERE; simple configuration files allow it to be extended to support several other instruments and testbeds. pyZELDA also includes simple simulation tools to measure the theoretical sensitivity of a sensor and to compare it to other sensors.

[ascl:1608.002] pyXSIM: Synthetic X-ray observations generator

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.

[ascl:1402.034] PyWiFeS: Wide Field Spectrograph data reduction pipeline

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.

[ascl:1402.004] PyVO: Python access to the Virtual Observatory

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.

[ascl:1907.003] pyuvdata: Pythonic interface to interferometric data sets

pyuvdata defines a pythonic interface to interferometric data sets; it supports the development of and interchange of data between calibration and foreground subtraction pipelines. It can read and write MIRIAD (ascl:1106.007), uvfits, and uvh5 files and reads CASA (ascl:1107.013) measurement sets and FHD (Fast Holographic Deconvolution) visibility save files. Particular focus has been paid to supporting drift and phased array modes.

[ascl:1810.009] PyUltraLight: Pseudo-spectral Python code to compute ultralight dark matter dynamics

PyUltraLight computes non-relativistic ultralight dark matter dynamics in a static spacetime background. It uses pseudo-spectral methods to compute the evolution of a complex scalar field governed by the Schrödinger-Poisson system of coupled differential equations. Computations are performed on a fixed-grid with periodic boundary conditions, allowing for a decomposition of the field in momentum space by way of the discrete Fourier transform. The field is then evolved through a symmetrized split-step Fourier algorithm, in which nonlinear operators are applied in real space, while spatial derivatives are computed in Fourier space. Fourier transforms within PyUltraLight are handled using the pyFFTW pythonic wrapper around FFTW (ascl:1201.015).

[ascl:1710.010] PyTransport: Calculate inflationary correlation functions

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.

[ascl:1505.024] PyTransit: Transit light curve modeling

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.

[ascl:1501.010] PythonPhot: Simple DAOPHOT-type photometry in Python

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.

[ascl:1501.003] python-qucs: Python package for automating QUCS simulations

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.

[ascl:1612.001] Python-CPL: Python interface for the ESO Common Pipeline Library

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.

[ascl:1303.023] pysynphot: Synthetic photometry software package

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 (ascl:1207.011), 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.

[ascl:1109.001] PySpecKit: Python Spectroscopic Toolkit

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.

[ascl:1411.002] pysovo: A library for implementing alerts triggered by VOEvents

pysovo contains basic tools to work with VOEvents. Though written for specific needs, others interested in VOEvents may find it useful to examine.

[ascl:1503.008] pYSOVAR: Lightcurves analysis

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.

[ascl:1704.007] PySM: Python Sky Model

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.

[ascl:1805.026] PySE: Python Source Extractor for radio astronomical images

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).

[ascl:1908.024] PYSAT: Python Satellite Data Analysis Toolkit

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.

[ascl:1207.010] PySALT: SALT science pipeline

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.

[ascl:1904.026] pyRSD: Accurate predictions for the clustering of galaxies in redshift-space in Python

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.

[ascl:1507.018] pyro: Python-based tutorial for computational methods for hydrodynamics

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.

[submitted] pyreaclib

A python interface to the JINA reaclib nuclear reaction database

[ascl:1207.011] PyRAF: Python alternative for IRAF

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.

[ascl:1602.002] pyraf-dbsp: Reduction pipeline for the Palomar Double Beam Spectrograph

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.

[ascl:1908.009] PyRADS: Python RADiation model for planetary atmosphereS

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.

[ascl:1807.006] pyqz: Emission line code

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.

[ascl:1809.008] PyQSOFit: Python code to fit the spectrum of quasars

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.

[ascl:1706.011] PyPulse: PSRFITS handler

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

[ascl:1612.005] PyProfit: Wrapper for libprofit

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

[ascl:1609.022] PyPHER: Python-based PSF Homogenization kERnels

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.

[ascl:1905.027] PyPDR: Python Photo Dissociation Regions

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.

[ascl:1802.012] PyOSE: Orbital sampling effect (OSE) simulator

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.

[ascl:1612.008] PyORBIT: Exoplanet orbital parameters and stellar activity

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.

[ascl:1501.001] PynPoint: Exoplanet image data analysis

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.

[ascl:1812.010] PynPoint 0.6.0: Pipeline for processing and analysis of high-contrast imaging data

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).

[ascl:1304.021] PyNeb: Analysis of emission lines

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.

[ascl:1305.002] pynbody: N-Body/SPH analysis for python

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.

[ascl:1703.009] PyMVPA: MultiVariate Pattern Analysis in Python

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.

[ascl:1806.028] PyMUSE: VLT/MUSE data analyzer

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.

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