Results 601-650 of 2176 (2134 ASCL, 42 submitted)
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.
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).
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.
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.
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.
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.
A python interface to the JINA reaclib nuclear reaction database
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.
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.
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.
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 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.
PyPulse handles PSRFITS files and performs subsequent analyses on pulse profiles.
pyprofit is a python wrapper for libprofit (ascl:1612.003).
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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
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.
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).
PyMORESANE is a Python and pyCUDA-accelerated implementation of the MORESANE deconvolution algorithm, a sparse deconvolution algorithm for radio interferometric imaging. It can restore diffuse astronomical sources which are faint in brightness, complex in morphology and possibly buried in the dirty beam’s side lobes of bright radio sources in the field.
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).
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.
PyMieDAP (Python Mie Doubling Adding Program) makes light scattering computations with Mie scattering and radiative transfer computations with full orders of scattering and taking into account the polarization of the light scattered. Full planet modeling at any phase angle is possible. With the included subpackage exopy, it is also possible to simulate systems with a star, a planet and a possible moon.
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.
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.
PyMF performs spatial filtering (matched filter, matched multifilter, constrained matched filter and constrained matched mutifilter) image processing that provides optimal reduction of the contamination introduced by sources that can be approximated by templates. These techniques use the flat-sky approximation.
pyMCZ calculates metallicity according to a number of strong line metallicity diagnostics from spectroscopy line measurements and obtains 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.
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.
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.
pyLIMA (python Lightcurve Identification and Microlensing Analysis) fits microlensing lightcurves and derives the physical quantities of lens systems. The package provides microlensing modeling, and the magnification estimation for high cadence lightcurves has been optimized. pyLIMA is designed to make microlensing modeling and event simulation widely available to the community.
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).
Pylians facilitates the analysis of numerical simulations (both N-body and hydro). This set of libraries, written in python, cython and C, compute power spectra, bispectra, and correlation functions, identifies voids, and populates halos with galaxies using an HOD. Pylians can also apply HI+H2 corrections to the output of hydrodynamic simulations, makes 21cm maps, computes DLAs column density distribution functions, and plots density fields.
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.
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.
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.
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.
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.
PyHammer performs rapid and automatic spectral classification of stars according to the Morgan-Keenan classification system; it is a Python revision of the IDL code The Hammer (ascl:1405.003) and offers additional capabilities. Working in the range of 3,650-10,200 Angstroms, the automatic spectral typing algorithm compares important spectral lines to template spectra and determines the best matching spectral type, ranging from O to L type stars. The code can also determine a star's metallicity ([Fe/H]) and radial velocity shifts. Once the automatic classification algorithm has run, PyHammer provides the user an interface for determining spectral types visually by comparing their spectra to provided templates.
pyGTC creates giant triangle confusogram (GTC) plots. Triangle plots display the results of a Monte-Carlo Markov Chain (MCMC) sampling or similar analysis. The recovered parameter constraints are displayed on a grid in which the diagonal shows the one-dimensional posteriors (and, optionally, priors) and the lower-left triangle shows the pairwise projections. Such plots are useful for seeing the parameter covariances along with the priors when fitting a model to data.
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).
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.
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.
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