Results 2451-2500 of 3572 (3481 ASCL, 91 submitted)

[ascl:1204.002]
pyBLoCXS: Bayesian Low-Count X-ray Spectral analysis

Siemiginowska, Aneta; Kashyap, Vinay; Refsdal, Brian; van Dyk, David; Connors, Alanna; Park, Taeyoung

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.

[ascl:2306.057]
pybranch: Calculate experimental branching fractions and transition probabilities from atomic spectra

pybranch calculates experimental branching fractions and transition probabilities from measurements of atomic spectra. Though the program is usually used with spectral line lists from intensity-calibrated spectra from Fourier transform spectrometers, it can in principle be used with any calibrated spectra that meet the input requirements. pybranch takes a set of linelists, computes a weighted average branching fraction (Fki) for each line, combines these branching fractions with the level lifetime to obtain the transition probability, and then prints the calibrated intensities and S/N ratios for all the lines observed from a particular upper level in each spectrum. One line can be chosen to use as a reference to put all of the intensities on the same scale. pybranch can use calculated transition probabilities to calculate a residual from lines that have not been observed.

[ascl:2312.025]
pyC^{2}Ray: Python interface to C^{2}Ray with GPU acceleration

Hirling, Patrick; Bianco, Michele; Giri, Sambit K.; Iliev, Ilian T.; Mellema, Garrelt; Kneib, Jean-Paul

pyC^{2}Ray updates C^{2}-Ray (ascl:2312.022), an astrophysical radiative transfer code used to simulate the Epoch of Reionization (EoR). pyC^{2}Ray includes a new raytracing method, ASORA, developed for GPUs, and provides a Python interface for customizable use of the code. The core features of C^{2}-Ray, written in Fortran90, are wrapped using f2py as a Python extension module, while the raytracing library ASORA is implemented in C++ using CUDA. Both are native Python C-extensions and can be directly accessed from any Python script.

[ascl:2107.017]
PyCactus: Post-processing tools for Cactus computational toolkit simulation data

PyCactus contains tools for postprocessing data from numerical simulations performed with the Einstein Toolkit, based on the Cactus computational toolkit. The main package is PostCactus, which provides a high-level Python interface to the various data formats in a simulation folder. Further, the package SimRep allows the automatic creation of html reports for a simulation, and the SimVideo package allows the creation of movies visualizing simulation data.

[ascl:2206.021]
PyCASSO2: Stellar population and emission line fits in integral field spectra

de Amorim, André Luiz; Vale Asari, Natalia; Cid Fernandes, Roberto; García-Benito, Rubén; Werle, Ariel

PyCASSO runs the STARLIGHT code (ascl:1108.006) in integral field spectra (IFS). Cubes from various instruments are supported, including PMAS/PPAK (CALIFA), MaNGA, GMOS and MUSE. Emission lines can be measured using DOBBY, which is included in the package. The package also includes tools for IFS cubes analysis and plotting.

[ascl:1805.030]
PyCBC: Gravitational-wave data analysis toolkit

PyCBC analyzes data from gravitational-wave laser interferometer detectors, finds signals, and studies their parameters. It contains algorithms that can detect coalescing compact binaries and measure the astrophysical parameters of detected sources. PyCBC was used in the first direct detection of gravitational waves by LIGO and is used in the ongoing analysis of LIGO and Virgo data.

[ascl:1805.032]
PyCCF: Python Cross Correlation Function for reverberation mapping studies

PyCCF emulates 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 Monto 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.

[ascl:2112.001]
pycelp: Python package for Coronal Emission Line Polarization

pyCELP (aka "pi-KELP") calculates Coronal Emission Line Polarization. It forward synthesizes the polarized emission of ionized atoms formed in the solar corona and calculates the atomic density matrix elements for a single ion under coronal equilibrium conditions and excited by a prescribed radiation field and thermal collisions. pyCELP solves a set of statistical equilibrium equations in the spherical statistical tensor representation for a multi-level atom for the no-coherence case. This approximation is useful in the case of forbidden line emission by visible and infrared lines, such as Fe XIII 1074.7 nm and Si X 3934 nm.

[submitted]
pycf3 - Cosmicflows-3 Distance-Velocity Calculator client for Python

The project is a simple Python client for Cosmicflows-3 Distance-Velocity Calculator at distances less than 400 Mpc (http://edd.ifa.hawaii.edu/CF3calculator/)

Compute expectation distances or velocities based on smoothed velocity field from the Wiener filter model of https://ui.adsabs.harvard.edu/abs/2019MNRAS.488.5438G/abstract.

[ascl:2312.034]
pycheops: Light curve analysis for ESA CHEOPS data

Maxted, P. F. L.; Ehrenreich, D.; Wilson, T. G.; Alibert, Y.; Cameron, A. Collier; Hoyer, S.; Sousa, S. G.; Olofsson, G.; Bekkelien, A.; Deline, A.; Delrez, L.; Bonfanti, A.; Borsato, L.; Alonso, R.; Anglada Escudé, G.; Barrado, D.; Barros, S. C. C.; Baumjohann, W.; Beck, M.; Beck, T.; Benz, W.; Billot, N.; Biondi, F.; Bonfils, X.; Brandeker, A.; Broeg, C.; Bárczy, T.; Cabrera, J.; Charnoz, S.; Corral Van Damme, C.; Csizmadia, Sz; Davies, M. B.; Deleuil, M.; Demangeon, O. D. S.; Demory, B. -O.; Erikson, A.; Florén, H. G.; Fortier, A.; Fossati, L.; Fridlund, M.; Futyan, D.; Gandolfi, D.; Gillon, M.; Guedel, M.; Guterman, P.; Heng, K.; Isaak, K. G.; Kiss, L.; Laskar, J.; Lecavelier des Etangs, A.; Lendl, M.; Lovis, C.; Magrin, D.; Nascimbeni, V.; Ottensamer, R.; Pagano, I.; Pallé, E.; Peter, G.; Piotto, G.; Pollacco, D.; Pozuelos, F. J.; Queloz, D.; Ragazzoni, R.; Rando, N.; Rauer, H.; Reimers, C.; Ribas, I.; Salmon, S.; Santos, N. C.; Scandariato, G.; Simon, A. E.; Smith, A. M. S.; Steller, M.; Swayne, M. I.; Szabó, Gy M.; Ségransan, D.; Thomas, N.; Udry, S.; Van Grootel, V.; Walton, N. A.

pycheops analyzes CHEOPS light curve data. The models in the package can also be applied to other types of data. pycheops includes a "cook book" and examples; in addition, it provides a command-line tool that aids in the preparation of observing requests for CHEOPS observers.

[submitted]
Pyckles

A super lightweight interface in Python to load spectra from the Pickles 1998 (stellar) and Brown 2014 (galactic) spectral catalogues

[ascl:1304.020]
pyCloudy: Tools to manage astronomical Cloudy photoionization code

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:

- define and write input file(s) for Cloudy code. As you can have it in a code, you may generate automatically sets of input files, changing parameters from one to the other.<

- read the Cloudy output files and play with the data: you will be able to plot line emissivity ratio vs. the radius of the nebula, the electron temperature, or any Cloudy output.

- build pseudo-3D models, a la Cloudy_3D, by running a set of models, changing parameters (e.g. inner radius, density) following angular laws, reading the outputs of the set of models and interpolating the results (Te, ne, line emissivities) in a 3D cube.

[ascl:1509.007]
pycola: N-body COLA method code

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.

[ascl:2303.007]
PyCom: Interstellar communication

PyCom provides function calls for deriving the optimal communication scheme to maximize the data rate between a remote probe and home-base. It includes models for the loss of photons from diffraction, technological limitations, interstellar extinction and atmospheric transmission, and manages major atmospheric, zodiacal, stellar and instrumental noise sources. It also includes scripts for creating figures appearing in the referenced paper.

[ascl:1311.002]
PyCOOL: Cosmological Object-Oriented Lattice code

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.

[ascl:2403.009]
pycorr: Two-point correlation function estimation

pycorr wraps two-point counter engines such as Corrfunc (ascl:1703.003) to estimate the correlation function. It supports theta (angular), s, s-mu, rp-pi binning schemes, analytical two-point counts with periodic boundary conditions, and inverse bitwise weights (in any integer format) and (angular) upweighting. It also provides MPI parallelization and jackknife estimate of the correlation function covariance matrix.

[ascl:2407.003]
pycosie: Python analysis code used on Technicolor Dawn

pycosie is analysis code used for Technicolor Dawn (TD), a Gadget-3 derived cosmological radiative SPH simulation suite. The target analyses are to complement what is done with TD and other analysis software in its suite. pycosie creates power spectrum from generated Lyman-alpha forests spectra, links absorbers to potential host galaxies, grids gas information for each galaxy, and reads specific output files from software such as Rockstar (ascl:1210.008) and SKID (ascl:1102.020).

[ascl:1210.027]
PyCosmic: Detecting cosmics in CALIFA and other fiber-fed integral-field spectroscopy datasets

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.

[ascl:2004.007]
PyCosmo: Multi-purpose cosmology calculation tool

Tarsitano, Federica; Schmitt, Uwe; Refregier, Alexandre; Akeret, Joel; Amara, Adam; Gamper, Lukas; Nicola, Andrina

PyCosmo provides accurate predictions for cosmological observables including background quantities, power spectra and Limber and beyond-Limber angular power spectra. The software is designed to be interactive and user-friendly. It is available for download and is also offered on an interactive platform (PyCosmo Hub), which allows users to perform their own computations using Jupyter Notebooks without installing any software.

[ascl:1810.008]
pycraf: Spectrum-management compatibility

The pycraf Python package provides functions and procedures for spectrum-management compatibility studies, such as calculating the interference levels at a radio telescope produced from a radio broadcasting tower. It includes an implementation of ITU-R Recommendation P.452-16 for calculating path attenuation for the distance between an interferer and the victim service. It supports NASA's Shuttle Radar Topography Mission (SRTM) data for height-profile generation, includes a full implementation of ITU-R Rec. P.676-10, which provides two atmospheric models to calculate the attenuation for paths through Earth's atmosphere, and provides various antenna patterns necessary for compatibility studies (e.g., RAS, IMT, fixed-service links). The package can also convert power flux densities, field strengths, transmitted and received powers at certain distances and frequencies into each other.

[ascl:2307.040]
pycrires: Data reduction pipeline for VLT/CRIRES+

pycrires runs the CRIRES+ recipes of EsoRex. The pipeline organizes the raw data, creates SOF and configuration files, runs the calibration and science recipes, and creates plots of the images and extracted spectra. Additionally, it corrects remaining inaccuracies in the wavelength solution and the spectrum curvature. pycrires also provides dedicated routines for the extraction, calibration, and detection of spatially-resolved objects such as directly imaged planets.

[ascl:1509.010]
PyCS : Python Curve Shifting

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.

[submitted]
pydftools: Distribution function fitting in Python

pydftools is a pure-python port of the dftools R package (ascl:1805.002), which finds the most likely P parameters of a D-dimensional distribution function (DF) generating N objects, where each object is specified by D observables with measurement uncertainties. For instance, if the objects are galaxies, it can fit a MF (P=1), a mass-size distribution (P=2) or the mass-spin-morphology distribution (P=3). Unlike most common fitting approaches, this method accurately accounts for measurement in uncertainties and complex selection functions. Though this package imitates the dftools package quite closely while being as Pythonic as possible, it has not implemented 2D+ nor non-parametric.

[ascl:2106.003]
PyDoppler: Wrapper for Doppler tomography software

PyDoppler is a python-based wrapper for the Spruit Doppler tomography software dopmap (ascl:2106.002). PyDoppler is designed to study time-resolved spectroscopic datasets of accreting compact binaries. This code can produce a trail spectra of a dataset and create Doppler tomography maps. It is intended to be a light-weight code for single emission line datasets.

[ascl:1401.005]
PyDrizzle: Python version of Drizzle

PyDrizzle provides a semi-automated interface for computing the parameters necessary for running Drizzle (ascl:1212.011). 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.

[ascl:2103.008]
Pyedra: Python implementation for asteroid phase curve fitting

Pyedra performs asteroid phase curve fitting. From a simple table containing the asteroid MPC number, phase angle and reduced magnitude, Pyedra estimates the parameters of the phase function using the least squares method. The user can choose from three different models for the phase curve fit: H-G model, H-G1-G2 model and the Shevchenko model. The output in all cases is a table containing the adjusted parameters and their corresponding errors. This package allows carrying out phase function analysis for a few asteroids as well as to process large volumes of data such as those released by current large surveys.

[ascl:1112.014]
PyEphem: Astronomical Ephemeris for Python

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 XEphem astronomy application (ascl:1112.013), whose author, Elwood Downey, generously gave permission for us to use them as the basis for PyEphem.

[ascl:1609.025]
PYESSENCE: Generalized Coupled Quintessence Linear Perturbation Python Code

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.

[ascl:2301.013]
pyExoRaMa: An interactive tool to investigate the radius-mass diagram for exoplanets

pyExoRaMa visualizes and manipulates data related to exoplanets and their host stars in a multi-dimensional parameter space. It enables statistical studies based on the large and constantly increasing number of detected exoplanets, identifies possible interdependence among several physical parameters, and compares observables with theoretical models describing the exoplanet composition and structure.

[ascl:1403.002]
pyExtinction: Atmospheric extinction

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.

[ascl:2407.002]
pyFAT: Python Fully Automated TiRiFiC

Python Fully Automated TiRiFiC (pyFAT) wraps around the tilted ring fitting code (TiRiFiC, ascl:1208.008) to fully automate the process of fitting simple tilted ring models to line emission cubes. pyFAT is the successor to the IDL/GDL FAT (ascl:1507.011) code and offers improved handling and fitting as well as several new features. PyFAT fits simple rotationally symmetric discs with asymmetric warps and surface brightness distributions, providing a base model that can can be used in TiRiFiC to explore large scale motions. pyFAT delivers much more control over the fitting procedure, which is made possible by the new modular setup and the use of omegaconf for the input and default settings.

[ascl:2109.009]
pyFFTW: Python wrapper around FFTW

pyFFTW is a pythonic wrapper around FFTW (ascl:1201.015), the speedy FFT library. Both the complex DFT and the real DFT are supported, as well as on arbitrary axes of arbitrary shaped and strided arrays, which makes it almost feature equivalent to standard and real FFT functions of numpy.fft. Additionally, it supports the clongdouble dtype, which numpy.fft does not, and operating FFTW in multithreaded mode.

[ascl:1207.009]
PyFITS: Python FITS Module

Barrett, Paul; Hsu, J. C.; Hanley, Chris; Taylor, James; Droettboom, Michael; Bray, Erik M.; Hack, Warren; Greenfield, Perry; Wyckoff, Eric; Jedrzejewski, Robert; De La Pena, Michele; Hodge, Phil

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.

**PyFITS has been deprecated. Please see Astropy**.

[ascl:1103.012]
Pyflation: Second Order Perturbations During Inflation Beyond Slow-roll

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).

[submitted]
PyFOSC: a pipeline toolbox for BFOSC/YFOSC long-slit spectroscopy data reduction

PyFOSC is a pipeline toolbox for long-slit spectroscopy data reduction written in Python. It can be used for FOSC (Faint Object Spectrograph and Camera) data from Xinglong/Lijiang 2-meter telescopes in China. This pipeline privodes a neat way for data pre-processing, including updating missing header fileds for BFOSC data, reducing fits file extension for YFOSC data, etc. And it makes the data reduction procedure efficient by using previously identified lamp spectra as re-identification references during wavelength calibration, and applying multiprocessing in some modules. PyFOSC also enables customization for any other long-slit spectroscopy data.

[ascl:2102.027]
PyFstat: Continuous gravitational-wave data analysis

PyFstat performs F-statistic-based continuous gravitational wave (CW) searches and other CW data analysis tasks. It is built on top of the LALSuite library (ascl:2012.021), making that library's functionality more accessible through a Python interface; it also provides MCMC-based followup of promising candidates from wide-parameter-space searches.

[ascl:2203.005]
pygacs: Toolkit to manipulate Gaia catalog tables

pygacs manipulates Gaia catalog tables hosted at ESA's Gaia Archive Core Systems (GACS). It provides python modules for the access and manipulation of tables in GACS, such as a basic query on a single table or crossmatch between two tables. It employs the TAP command line access tools described in the Help section of the GACS web pages. Both public and authenticated access have been implemented.

[ascl:1811.014]
pygad: Analyzing Gadget Simulations with Python

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.

[ascl:1411.001]
pyGadgetReader: GADGET snapshot reader for python

pyGadgetReader is a universal GADGET snapshot reader for python that supports type-1, type-2, HDF5, and TIPSY (ascl:1111.015) binary formats. It additionally supports reading binary outputs from FoF_Special, P-StarGroupFinder, Rockstar (ascl:1210.008), and Rockstar-Galaxies.

[ascl:1603.013]
PyGDSM: Python interface to Global Diffuse Sky Models

PyGDSM (formely 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). PyGDSM uses the GSM to generate all-sky maps in Healpix format of diffuse Galactic radio emission from 10 MHz to 94 GHz. The PyGDSM module provides visualization utilities, file output in FITS format, and the ability to generate observed skies for a given location and date. PyGDSM requires Healpy (ascl:2008.022), PyEphem (ascl:1112.014), and AstroPy (ascl:1304.002).

[ascl:1402.021]
PyGFit: Python Galaxy Fitter

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.

[ascl:1611.013]
pyGMMis: Mixtures-of-Gaussians density estimation method

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.

[ascl:1907.004]
pyGTC: Parameter covariance plots

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.

[ascl:2311.013]
pygwb: Lighweight python stochastic GWB analysis pipeline

Renzini, Arianna I.; Romero-Rodríguez, Alba; Talbot, Colm; Lalleman, Max; Kandhasamy, Shivaraj; Turbang, Kevin; Biscoveanu, Sylvia; Martinovic, Katarina; Meyers, Patrick; Tsukada, Leo; Janssens, Kamiel; Davis, Derek; Matas, Andrew; Charlton, Philip; Liu, Guo-Chin; Dvorkin, Irina; Banagiri, Sharan; Bose, Sukanta; Callister, Thomas; De Lillo, Federico; D'Onofrio, Luca; Garufi, Fabio; Harry, Gregg; Lawrence, Jessica; Mandic, Vuk; Macquet, Adrian; Michaloliakos, Ioannis; Mitra, Sanjit; Pham, Kiet; Poggiani, Rosa; Regimbau, Tania; Romano, Joseph D.; van Remortel, Nick; Zhong, Haowen

pygwb analyzes laser interferometer data and designs a gravitational wave background (GWB) search pipeline. Its modular and flexible codebase is tailored to current ground-based interferometers such as LIGO Hanford, LIGO Livingston, and Virgo, but can be generalized to other configurations. It is based on GWpy (ascl:1912.016) and bilby (ascl:1901.011) for optimal integration with widely-used gravitational wave data analysis tools. pygwb also includes a set of scripts to analyze data and perform large-scale searches on a high-performance computing cluster efficiently.

[ascl:2007.020]
pygwinc: Gravitational Wave Interferometer Noise Calculator

pygwinc processes and plots noise budgets for ground-based gravitational wave detectors. Its primary feature is a collection of mostly analytic noise calculation functions for various sources of noise affecting detectors, including quantum and seismic noise, mirror coating and substrate thermal noise, suspension fiber thermal noise, and residual gas noise. It is also a generalized noise budgeting tool that allows users to create arbitrary noise budgets for any experiment, not just ground-based GW detectors, using measured or analytically calculated data.

[ascl:2307.025]
pyhalomodel: Halo-model implementation for power spectra

pyhalomodel computes halo-model power spectra for any desired tracer combination. The software requires only halo profiles for the tracers to be specified; these could be matter profiles, galaxy profiles, or something else, such as electron-pressure or HI profiles. pyhalomodel makes it easier to perform basic calculations using the halo model by reducing the changes of variables required to integrate halo profiles against halo mass functions, which can be confusing and tedious.

[ascl:2002.011]
PyHammer: Python spectral typing suite

Kesseli, Aurora; West, Andrew; Veyette, Mark; Harrison, Brandon; Feldman, Dan; Morgan, Dylan; Theissen, Chris; Robinson, Connor

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.

[ascl:2206.010]
pyHIIexplorerV2: Integrated spectra of HII regions extractor

Espinosa-Ponce, C.; Sánchez, S. F.; Morisset, C.; Barrera-Ballesteros, J. K.; Galbany, L.; García-Benito, R.; Lacerda, E. A. D.; Mast, D.

pyHIIexplorerV2 extracts the integrated spectra of HII regions from integral field spectroscopy (IFS) datacubes. The detection of HII regions performed by pyHIIexplorer is based on two assumptions: 1) HII regions have strong emission lines that are clearly above the continuum emission and the average ionized gas emission across each galaxy, and 2) the typical size of HII regions is about a few hundreds of parsecs, which corresponds to a usual projected size of a few arcsec at the distance of our galaxies. These assumptions will define clumpy structures with a high Ha emission line contrast in comparison to the continuum. pyHIIexplorerV2 is written in Python; it is based on and is a successor to HIIexplorer (ascl:1603.017).

[ascl:1511.005]
pyhrs: Spectroscopic data reduction package for SALT

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.

[ascl:2109.008]
pyia: Python package for working with Gaia data

pyia provides tools for working with Gaia data. It accesses Gaia data columns as Quantity objects, *i.e.*, with units (*e.g.*, data.parallax will have units ‘milliarcsecond’)
, constructs covariance matrices for Gaia data, and generates random samples from the Gaia error distribution per source. pyia can also create SkyCoord objects from Gaia data and execute simple (small) remote queries via the Gaia science archive and automatically fetch the results.

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