➥ Tip! Refine or expand your search. Authors are sometimes listed as 'Smith, J. K.' instead of 'Smith, John' so it is useful to search for last names only. Note this is currently a simple phrase search.
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.
Astropy provides a common framework, core package of code, and affiliated packages for astronomy in Python. Development is actively ongoing, with major packages such as PyFITS, PyWCS, vo, and asciitable already merged in. Astropy is intended to contain much of the core functionality and some common tools needed for performing astronomy and astrophysics with Python.
The Semi-automated multi-COmponent Universal Spectral-line fitting Engine (SCOUSE) is a spectral line fitting algorithm that fits Gaussian files to spectral line emission. It identifies the spatial area over which to fit the data and generates a grid of spectral averaging areas (SAAs). The spatially averaged spectra are fitted according to user-provided tolerance levels, and the best fit is selected using the Akaike Information Criterion, which weights the chisq of a best-fitting solution according to the number of free-parameters. A more detailed inspection of the spectra can be performed to improve the fit through an iterative process, after which SCOUSE integrates the new solutions into the solution file.
CAMELOT facilitates the comparison of observational data and simulations of molecular clouds and/or star-forming regions. The central component of CAMELOT is a database summarizing the properties of observational data and simulations in the literature through pertinent metadata. The core functionality allows users to upload metadata, search and visualize the contents of the database to find and match observations/simulations over any range of parameter space.
To bridge the fundamental disconnect between inherently 2D observational data and 3D simulations, the code uses key physical properties that, in principle, are straightforward for both observers and simulators to measure — the surface density (Sigma), velocity dispersion (sigma) and radius (R). By determining these in a self-consistent way for all entries in the database, it should be possible to make robust comparisons.
Spectral-cube provides an easy way to read, manipulate, analyze, and write data cubes with two positional dimensions and one spectral dimension, optionally with Stokes parameters. It is a versatile data container for building custom analysis routines. It provides a uniform interface to spectral cubes, robust to the wide range of conventions of axis order, spatial projections, and spectral units that exist in the wild, and allows easy extraction of cube sub-regions using physical coordinates. It has the ability to create, combine, and apply masks to datasets and is designed to work with datasets too large to load into memory, and provide basic summary statistic methods like moments and array aggregates.
Given a path defined in sky coordinates and a spectral cube, pvextractor extracts a slice of the cube along that path and along the spectral axis to produce a position-velocity or position-frequency slice. The path can be defined programmatically in pixel or world coordinates, and can also be drawn interactively using a simple GUI. Pvextractor is the main function, but also includes a few utilities related to header trimming and parsing.
SCIMES identifies relevant molecular gas structures within dendrograms of emission using the spectral clustering paradigm. It is useful for decomposing objects in complex environments imaged at high resolution.
Astroquery allows users to access online astronomical data from a wide range of sources; it is an Astropy-affiliated package. Each web service has its own sub-package for interfacing with a particular data source.
STATCONT determines the continuum emission level in line-rich spectral data by inspecting the intensity distribution of a given spectrum by using different statistical approaches. The sigma-clipping algorithm provides the most accurate continuum level determination, together with information on the uncertainty in its determination; this uncertainty is used to correct the final continuum emission level. In general, STATCONT obtains accuracies of < 10 % in the continuum determination, and < 5 % in most cases. The main products of the software are the continuum emission level, together with its uncertainty, and data cubes containing only spectral line emission, i.e. continuum-subtracted data cubes. STATCONT also includes the option to estimate the spectral index or variation of the continuum emission with frequency.
HiGal SED Fitter fits modified blackbody SEDs to Herschel data, specifically targeted at Herschel Hi-Gal data.
TurbuStat implements a variety of turbulence-based statistics described in the astronomical literature and defines distance metrics for each statistic to quantitatively compare spectral-line data cubes, as well as column density, integrated intensity, or other moment maps. The software can simulate observations of fractional Brownian Motion fields, including 2-D images and optically thin H I data cubes. TurbuStat also offers multicore fast-Fourier-transform support and provides a segmented linear model for fitting lines with a break point.
Astrodendro, written in Python, creates dendrograms for exploring and displaying hierarchical structures in observed or simulated astronomical data. It handles noisy data by allowing specification of the minimum height of a structure and the minimum number of pixels needed for an independent structure. Astrodendro allows interactive viewing of computed dendrograms and can also produce publication-quality plots with the non-interactive plotting interface.
sf3dmodels models star-forming regions; it brings together analytical models in order to compute their physical properties in a 3-dimensional grid. The package can couple different models in a single grid to recreate complex star forming systems such as those being revealed by current instruments. The output data can be read with LIME (ascl:1107.012) or RADMC-3D (ascl:1108.016) to carry out radiative transfer calculations of the modeled region.
scousepy is a Python implementation of spectral line-fitting IDL code SCOUSE (ascl:1601.003). It fits a large amount of complex astronomical spectral line data in a systematic way.
acorns generates a hierarchical system of clusters within discrete data by using an n-dimensional unsupervised machine-learning algorithm that clusters spectroscopic position-position-velocity data. The algorithm is based on a technique known as hierarchical agglomerative clustering. Although acorns was designed with the analysis of discrete spectroscopic position-position-velocity (PPV) data in mind (rather than uniformly spaced data cubes), clustering can be performed in n-dimensions and the algorithm can be readily applied to other data sets in addition to PPV measurements.