Debian Astro Python3 packages

IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs.

This package contains the actual terminal shell for Python 3.

• Debian Python Team
• Gordon Ball

• 5.1.0-3+deb9u1 (stretch-security)
• 8.5.0-4 (bookworm)
• 7.20.0-1+deb11u1 (bullseye-security)
• 7.20.0-1+deb11u1 (bullseye)
• 2.3.0-2 (jessie)
• 5.1.0-3 (stretch)
• 8.29.0-1 (trixie)
• 8.29.0-1 (sid)
• 5.8.0-1+deb10u1 (buster-security)
• 5.8.0-1+deb10u1 (buster)

• 1367 regular users
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• isympy3
• python3-matplotlib
• python3-sympy

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APLpy is a Python module aimed at producing publication-quality plots of astronomical imaging data in FITS format. The module uses Matplotlib, a powerful and interactive plotting package. It is capable of creating output files in several graphical formats, including EPS, PDF, PS, PNG, and SVG.

• Debian Astronomy Team
• Ole Streicher

• 2.0.3-1 (buster)
• 2.1.0-1 (bookworm)
• 2.1.0-3 (trixie)
• 2.0.3-2 (bullseye)
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astLib is a set of Python3 modules that provides some tools for research astronomers. It can be used for astronomical plots, some statistics, common calculations, coordinate conversions, and manipulating FITS images with World Coordinate System (WCS) information through PyWCSTools - a simple wrapping of WCSTools by Jessica Mink. PyWCSTools is distributed (and developed) as part of astLib.

• Debian Astro Team
• Ole Streicher

• 0.11.5-1 (bullseye)
• 0.10.2-2 (buster)
• 0.11.8-1 (bookworm)
• 0.12.0-1 (trixie)
• 0.12.0-1 (sid)
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Python 3 module is to provide an easy way to compute dendrograms of observed or simulated Astronomical data in Python 3.

• Debian Astronomy Maintainers
• Ole Streicher

• 0.2.0+dfsg1-4 (trixie)
• 0.2.0+dfsg1-4 (bookworm)
• 0.2.0+dfsg1-2 (bullseye)
• 0.2.0+dfsg1-1 (buster)
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Astroplan is an observation planning package for astronomers that can help you plan for everything but the clouds.

It is an Astropy affiliated package that seeks to make your life as an observational astronomer a little less infuriating.

This is the Python 3 version of the package.

• Debian Astro Team
• Alexandre Detiste

• 0.10-1 (trixie)
• 0.2-5 (stretch)
• 0.10-1 (sid)
• 0.4-4 (buster)
• 0.7-4 (bookworm)
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A major part of the Astropy project is the concept of “Affiliated Packages”. An affiliated package is an astronomy-related Python package that is not part of the astropy core package, but has requested to be included as part of the Astropy project’s community. These packages are expressing an interest in Astropy’s goals of improving reuse, interoperability, and interface standards for Python astronomy and astrophysics packages.

This metapackage installs all affiliated packages.

• Debian Astro Team
• Ole Streicher

• 2.1 (trixie)
• 1.5 (buster)
• 1.2 (stretch)
• 1.5 (bullseye)
• 2.1 (sid)
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HEALPix is an acronym for Hierarchical Equal Area isoLatitude Pixelization of a sphere. As suggested in the name, this pixelization produces a subdivision of a spherical surface in which each pixel covers the same surface area as every other pixel. It is commonly used to store all-sky astronomical images, most famously maps of the cosmic microwave background.

This is a BSD-licensed HEALPix package developed by the Astropy project and based on C code written by Dustin Lang in astrometry.net.

This package provides modules for Python 3.

• Debian Astronomy Maintainers
• Ole Streicher

• 0.4-5 (buster)
• 0.6-2 (bookworm)
• 1.0.3-1 (sid)
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Astro-SCRAPPY is designed to detect cosmic rays in images (numpy arrays), based on Pieter van Dokkum's L.A.Cosmic algorithm. Parallelization is achieved using OpenMP.

• van Dokkum, P. G.: Cosmic-Ray Rejection by Laplacian Edge Detection. Publications of the Astronomical Society of the Pacific 113:1427 - 1420 (2001) (eprint)

• Debian Astronomy Team
• Ole Streicher

• 1.0.8-1 (buster)
• 1.2.0-1 (sid)
• 1.2.0-1 (trixie)
• 1.1.0-2 (bookworm)
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The casacore package contains the core libraries of the old AIPS++/CASA package. This split was made to get a better separation of core libraries and applications. CASA is now built on top of casacore.

This is the Python wrapper around the library.

• Debian Astro Team
• Ole Streicher

• 3.5.2-1 (bookworm)
• 3.0.0-1 (buster)
• 2.1.2-3 (stretch)
• 3.5.2-1 (sid)
• 3.3.1-1 (bullseye)
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This module can list, configure and execute CPL-based recipes from Python3. The input, calibration and output data can be specified as FITS files or as astropy fits objects in memory.

The Common Pipeline Library (CPL) comprises a set of ISO-C libraries that provide a comprehensive, efficient and robust software toolkit. It forms a basis for the creation of automated astronomical data-reduction tasks.

One of the features provided by the CPL is the ability to create data-reduction algorithms that run as plugins (dynamic libraries). These are called "recipes" and are one of the main aspects of the CPL data-reduction development environment.

• O. Streicher and P. M. Weilbacher: Python Bindings for the Common Pipeline Library. Astronomical Data Analysis Software and Systems XXI 461:853 (2012)

• Debian Astronomy Maintainers
• Phil Wyett

• 0.7.4-4 (sid)
• 0.7.2-1 (stretch)
• 0.7.4-2 (buster)
• 0.7.4-2 (bullseye)
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Dask is a flexible parallel computing library for analytics, containing two components.

1. Dynamic task scheduling optimized for computation. This is similar to Airflow, Luigi, Celery, or Make, but optimized for interactive computational workloads.
2. "Big Data" collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of the dynamic task schedulers.

This contains the Python 3 version.

• Debian Python Team
• Étienne Mollier

• 2022.12.1+dfsg-2 (bookworm)
• 2021.01.0+dfsg-1 (bullseye)
• 2024.5.2+dfsg-1 (trixie)
• 2024.5.2+dfsg-1 (sid)
• 1.0.0+dfsg-2 (buster)
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The drms module provides an easy-to-use Python interface for accessing HMI, AIA and MDI data with Python. It uses the publicly accessible Joint Science Operations Center (JSOC) server by default, but can also be used with local NetDRMS sites.

The Helioseismic and Magnetic Imager (HMI) and the Atmospheric Imaging Assembly (AIA) are instruments aboard the Solar Dynamics Observatory (SDO) designed to study oscillations and the magnetic field at the solar surface. The Michelson Doppler Imager (MDI) is the predecessor to the current HMI and was launched aboard the Solar and Heliospheric Observatory (SOHO). HMI and MDI observe the full solar disk at 6173 Å with high resolution, while AIA is designed to study the solar corona.

• Debian Astro Team
• Ole Streicher

• 0.8.0-1 (trixie)
• 0.8.0-1 (sid)
• 0.5.6-1 (buster)
• 0.6.3-1 (bookworm)
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• 13 regular users
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emcee is an extensible, pure-Python implementation of Goodman & Weare's Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler. It's designed for Bayesian parameter estimation.

• D. Foreman-Mackey, D.W. Hogg, D. Lang and J. Goodman: emcee: The MCMC Hammer. Publications of the Astronomical Society of the Pacific 125:306-312 (2013) (eprint)

• Debian Astronomy Team
• Ole Streicher

• 2.2.1-1 (stretch)
• 2.2.1-1 (buster)
• 3.1.6-1 (sid)
• 3.1.6-1 (trixie)
• 3.1.4-1 (bookworm)
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Extinction contains Cython-optimised implementations of empirical dust extinction laws found in the astronomical literature.

• Debian Astronomy Team
• Ole Streicher

• 0.4.6-3 (sid)
• 0.4.6-3 (trixie)
• 0.4.6-3 (bookworm)

• 7 regular users
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Fitsio provides an easy to use object-oriented API including providing a Numpy interface to FITS files read from CFITSIO. It allows direct access to the columns of a FITS binary table which can be useful for reading large fits files. Compared to astropy.io.fits, this package is closer to the underlying cfitsio library.

Fitsio also exposes a microlanguage (implemented in CFITSIO) for making efficient SQL-like queries of tables (single tables only though–no joins or anything like that).

• Debian Astronomy Team
• Ole Streicher

• 1.1.8+dfsg-1 (bookworm)
• 1.1.4+dfsg-1 (bullseye)
• 1.2.4+dfsg-1 (sid)
• 0.9.12+dfsg-1 (buster)
• 0.9.10+dfsg-1 (stretch)
• 1.2.4+dfsg-1 (trixie)

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Gammapy is an in-development affiliated package of Astropy that builds on the core scientific Python stack to provide tools to simulate and analyse the gamma-ray sky for telescopes such as CTA, H.E.S.S., VERITAS, MAGIC, HAWC and Fermi-LAT.

• C. Deil, R. Zanin, J. Lefaucheur, C. Boisson, B. Khélifi, R. Terrier, M. Wood, L. Mohrmann, N. Chakraborty, J. Watson, R. López Coto, S. Klepser, M. Cerruti, J.-P. Lenain, F. Acero, A. Djannati-Ataï, S. Pita, Z. Bosnjak, J. E. Ruiz, C. Trichard, T. Vuillaume, A. Donath, J. King, L. Jouvin, E. Owen, M. P. Arribas, B. Sipocz, D. Lennarz, A. Voruganti and M. Spir-Jacob: Gammapy - A prototype for the CTA science tools. ArXiv e-prints (2017) (eprint)

• Debian Astro Team
• Santiago Vila

• 1.0-2 (bookworm)
• 0.10-1 (buster)
• 0.17-2 (bullseye)
• 1.2-4 (trixie)
• 1.2-4 (sid)
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Ginga is a toolkit designed for building viewers for scientific image data in Python, visualizing 2D pixel data in numpy arrays. It can view astronomical data such as contained in files based on the FITS (Flexible Image Transport System) file format. It is written and is maintained by software engineers at the Subaru Telescope, National Astronomical Observatory of Japan.

The Ginga toolkit centers around an image display object which supports zooming and panning, color and intensity mapping, a choice of several automatic cut levels algorithms and canvases for plotting scalable geometric forms. In addition to this widget, a general purpose "reference" FITS viewer is provided, based on a plugin framework. A fairly complete set of standard plugins are provided for features that is expected from a modern FITS viewer: panning and zooming windows, star catalog access, cuts, star pick/fwhm, thumbnails, etc.

• Debian Astronomy Team
• Ole Streicher

• 5.2.0-1 (sid)
• 3.1.0-1 (bullseye)
• 2.7.2-2 (buster)
• 4.0.1-1 (bookworm)
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GWCS takes a general approach to WCS. It supports a data model which includes the entire transformation pipeline from input coordinates (detector by default) to world coordinates. The goal is to provide a flexible toolkit which is easily extendible by adding new transforms and frames.

This package contains the Python 3 version of the package.

• Debian Astro Team
• Phil Wyett

• 0.21.0-2 (sid)
• 0.21.0-2 (trixie)
• 0.18.3-1 (bookworm)
• 0.10.0-2 (buster)
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• 15 regular users
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HEALPix is an acronym for Hierarchical Equal Area isoLatitude Pixelization of a sphere. As suggested in the name, this pixelization produces a subdivision of a spherical surface in which each pixel covers the same surface area as every other pixel. It is commonly used to store all-sky astronomical images, most famously maps of the cosmic microwave background.

This package provides a Python3 wrapper around the C++ implementation of HEALPix.

• Debian Astro Team
• Michael R. Crusoe

• 1.16.6-2 (trixie)
• 1.10.3-2 (stretch)
• 1.16.6-2 (sid)
• 1.14.0-1 (bullseye)
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• 1.12.8-8 (buster)
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• 15 regular users
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Imexam is an affiliated package of AstroPy. It was designed to be a lightweight library which enables users to explore data using common methods which are consistent across viewers. It can be used from a command line interface, through a Jupyter notebook or through a Jupyter console. It can be used with multiple viewers, such as DS9 or Ginga, or without a viewer as a simple library to make plots and grab quick photometry information.

• Debian Astronomy Team
• Ole Streicher

• 0.9.1-6 (sid)
• 0.9.1-4 (bookworm)
• 0.9.1-1 (bullseye)
• 0.8.0-3 (buster)

• 10 regular users
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The lmfit Python package provides a simple, flexible interface to non-linear optimization or curve fitting problems. The package extends the optimization capabilities of scipy.optimize by replacing floating pointing values for the variables to be optimized with Parameter objects. These Parameters can be fixed or varied, have upper and/or lower bounds placed on its value, or written as an algebraic expression of other Parameters.

The principal advantage of using Parameters instead of simple variables is that the objective function does not have to be rewritten to reflect every change of what is varied in the fit, or what relationships or constraints are placed on the Parameters. This means a scientific programmer can write a general model that encapsulates the phenomenon to be optimized, and then allow user of that model to change what is varied and fixed, what range of values is acceptable for Parameters, and what constraints are placed on the model. The ease with which the model can be changed also allows one to easily test the significance of certain Parameters in a fitting model.

The lmfit package allows a choice of several optimization methods available from scipy.optimize. The default, and by far best tested optimization method used is the Levenberg-Marquardt algorithm from MINPACK-1 as implemented in scipy.optimize.leastsq. This method is by far the most tested and best support method in lmfit, and much of this document assumes this algorithm is used unless explicitly stated. An important point for many scientific analysis is that this is only method that automatically estimates uncertainties and correlations between fitted variables from the covariance matrix calculated during the fit.

A few other optimization routines are also supported, including Nelder-Mead simplex downhill, Powell's method, COBYLA, Sequential Least Squares methods as implemented in scipy.optimize.fmin, and several others from scipy.optimize. In their native form, some of these methods setting allow upper or lower bounds on parameter variables, or adding constraints on fitted variables. By using Parameter objects, lmfit allows bounds and constraints for all of these methods, and makes it easy to swap between methods without hanging the objective function or set of Parameters.

Finally, because the approach derived from MINPACK-1 usin the covariance matrix to determine uncertainties is sometimes questioned (and sometimes rightly so), lmfit supports methods to do a brute force search of the confidence intervals and correlations for sets of parameters.

This is the Python 3 version of the package.

• Debian Science Maintainers
• Andreas Tille

• 0.9.11+dfsg-2 (buster)
• 1.2.2-3 (trixie)
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This package provides a Python-3 wrapper to the Montage Astronomical Image Mosaic Engine, including both functions to access individual Montage commands, and high-level functions to facilitate mosaicking and re-projecting.

• Debian Astronomy Team
• Ole Streicher

• 0.9.9-2 (buster)
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This package provides functionality to make it easy to make your own scatter density maps, both for interactive and non-interactive use. Fast.

This package installs the library for Python 3.

• Debian Astronomy Maintainers
• Josue Ortega

• 0.7-1 (bullseye)
• 0.7-1 (trixie)
• 0.5-1 (buster)
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NumPy contains a powerful N-dimensional array object, sophisticated (broadcasting) functions, tools for integrating C/C++ and Fortran code, and useful linear algebra, Fourier transform, and random number capabilities.

This package installs NumPy for Python 3.

• Debian Python Team
• Stefano Rivera

• 1.26.4+ds-12 (trixie)
• 1.24.2-1+deb12u1 (bookworm)
• 1.19.5-1 (bullseye)
• 1.26.4+ds-12 (sid)
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• 1.16.2-1 (buster)
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• python3-shapely

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pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. pandas is well suited for many different kinds of data:

• Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet
• Ordered and unordered (not necessarily fixed-frequency) time series data.
• Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels
• Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure

This package contains the Python 3 version.

• McKinney, Wes: pandas: a Foundational Python Library for Data Analysis and Statistics. (2011) (eprint)

• Debian Science Team
• Rebecca N. Palmer

• 0.23.3+dfsg-3 (buster)
• 1.5.3+dfsg-2 (bookworm)
• 2.2.3+dfsg-5 (trixie)
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• 515 regular users
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• python3-bottleneck
• python3-sklearn-pandas

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• Debian Astro Team
• Ole Streicher

• 0.17.0-2 (bookworm)
• 0.11.1-1 (buster)
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• 15 regular users
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PyDL consists of Python replacements for functions that are part of the IDL built-in library or part of astronomical IDL libraries. The emphasis is on reproducing results of the astronomical library functions. Only the bare minimum of IDL built-in functions are implemented to support this.

There are four astronomical libraries targeted:

• idlutils: a general suite of tools heavily used by SDSS.

• Goddard utilities: The IDL Astronomy User's Library, maintained by Wayne Landsman and distributed with idlutils.

• idlspec2d: tools for working with SDSS, BOSS and eBOSS spectroscopic data.

• photoop: tools for working with SDSS imaging data.

This is the Python 3 version of the package.

• Debian Astro Team
• Alexandre Detiste

• 1.0.0~rc1-2 (bullseye)
• 0.5.3-3 (stretch)
• 0.6.0-3 (buster)
• 1.0.0~rc2-4 (bookworm)
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• 15 regular users
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Pythonic wrapper around FFTW, the speedy FFT library. The ultimate aim is to present a unified interface for all the possible transforms that FFTW can perform.

Both the complex DFT and the real DFT are supported, as well as arbitrary axes of abitrary shaped and strided arrays, which makes it almost feature equivalent to standard and real FFT functions of numpy.fft (indeed, it supports the clongdouble dtype which numpy.fft does not).

pyFFTW is BSD-licensed and should not be confused with python-fftw, a GPL-licensed python module with the same aim of providing python bindings to FFTW3. Or python3-gpyfft, which provides bindings to the OpenCL FFT library clFFT.

This package provides the Python 3 bindings.

• Debian Science Maintainers
• Andreas Tille

• 0.11.1-2 (buster)
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PyMOC provides a Python compatible library for handling MOCs.

Frequently astronomical survey catalogues or images are sparse and cover only a small part of the sky. In a Multi-Order Coverage map the extent of data in a particular dataset is cached as a pre-calculated mask image. The hierarchical nature enables fast boolean operations in image space, without needing to perform complex geometrical calculations. Services such as VizieR generally offer the MOC masks, allowing a faster experience in graphical applications such as Aladin, or for researchers quickly needing to locate which datasets may contain overlapping coverage.

The MOC mask image itself is tessellated and stored in NASA HealPix format, encoded inside a FITS image container. Using the HealPix (Hierarchical Equal Area isoLatitude Pixelization) tessellation method ensures that more precision (pixels) in the mask are available when describing complex shapes such as approximating survey or polygon edges, while only needing to store a single big cell/pixel when an coverage is either completely inside, or outside of the mask. Catalogues can be rendered on the mask as circles.

• Debian Astro Team
• Ole Streicher

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PyRAF is a command language for running IRAF tasks in a Python like environment. It works very similar to IRAF CL, but has been updated to allow such things as importing Python modules, GUI parameter editing and help. It can be imported into Python allowing you to run IRAF commands from within a larger script.

• Debian Astro Team
• Ole Streicher

• 2.1.15-2 (buster)
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pysynphot simulates photometric data and spectra as they are observed with the Hubble Space Telescope (HST). Passbands for standard photometric systems are available, and users can incorporate their own filters, spectra, and data. pysynphot user interface allows you to:

• Construct complicated composite spectra from various grids of model atmosphere spectra, parameterized spectrum models, and atlases of stellar spectrophotometry.
• Simulate observations.
• Query the resulting structures for quantities of interest, such as countrate, effective wavelength, effective stimulus, as well as the wavelength and flux arrays.
• Plot HST sensitivity curves and calibration target spectra.
• Compute photometric calibration parameters for any HST instrument mode.

pysynphot can help HST observers to perform cross-instrument simulations, to examine the transmission curve of the HST Optical Telescope Assembly (OTA), and spectra of HST calibration targets. Expert users can take advantage of the control and data structures available in Python to easily perform repetitive operations such as simulate the observation of multiple type of sources through multiple observing modes.

• P. L. Lim, R. I. Diaz and V. Laidler: PySynphot User’s Guide. (2015)

• Debian Astronomy Team
• Ole Streicher

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Radio Beam is a simple toolkit for reading beam information from FITS headers and manipulating beams. Some example applications include Convolution/deconvolution and Unit conversion (Jy to/from K).

• Debian Astronomy Team
• Alexandre Detiste

• 0.3.8-2 (sid)
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This is a package to reproject astronomical images using various techniques via a uniform interface. Reprojection means the re-gridding of images from one world coordinate system to another (for example changing the pixel resolution, orientation, coordinate system). Currently implemented are reprojection of celestial images by interpolation, as well as by finding the exact overlap between pixels on the celestial sphere. It can also reproject to/from HEALPIX projections.

This package contains the Python 3 version of the package.

• Debian Astronomy Team
• Phil Wyett

• 0.7.1-1 (bullseye)
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SEP makes the core algorithms of Source Extractor available as a library of stand-alone functions and classes. These operate directly on in-memory arrays (no FITS files or configuration files). The code is derived from the Source Extractor code base (written in C) and aims to produce results compatible with Source Extractor whenever possible.

This Python module that wraps the C library in a Pythonic API. The Python wrapper operates on NumPy arrays.

• Debian Astronomy Team
• Phil Wyett

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scikit-learn is a collection of Python modules relevant to machine/statistical learning and data mining. Non-exhaustive list of included functionality:

• Gaussian Mixture Models
• Manifold learning
• kNN
• SVM (via LIBSVM)

This package contains the Python 3 version.

• Debian Science Maintainers
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SNCosmo synthesizes supernova spectra and photometry from SN models, and has functions for fitting and sampling SN model parameters given photometric light curve data. It offers fast implementations of several commonly used extinction laws and can be used to construct SN models that include dust. The SNCosmo library includes supernova models such as SALT2, MLCS2k2, Hsiao, Nugent, PSNID, SNANA and Whalen models, as well as a variety of built-in bandpasses and magnitude systems, and provides convenience functions for reading and writing peculiar data formats used in other packages. The library is extensible, allowing new models, bandpasses, and magnitude systems to be defined using an object-oriented interface.

• Debian Astronomy Team
• Ole Streicher

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The specutils package implements base classes and utilities for interacting with astronomical spectra in Python and the Astropy project. It is intended for eventual merger with the astropy package, but for now is being developed independently.

• Debian Astronomy Team
• Ole Streicher

• 1.19.0-1 (sid)
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Theano is a Python library that allows one to define and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It provides a high-level Numpy like expression language for functional description of calculation, rearranges expressions for speed and stability, and generates native machine instructions for fast calculation. Optionally, highly accelerated computations could be carried out on graphics cards processors.

This package contains Theano for Python 3.

• Theano Development Team: Theano: A Python framework for fast computation of mathematical expressions. (2016) (eprint)

• Debian Science Maintainers
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The yt project aims to produce an integrated science environment for collaboratively asking and answering astrophysical questions. To do so, it will encompass the creation of initial conditions, the execution of simulations, and the detailed exploration and visualization of the resultant data. It will also provide a standard framework based on physical quantities interoperability between codes.

This package contains the Python 3 version of the package.

• M.J. Turk, B.D. Smith, J.S. Oishi, S. Skory, S.W. Skillman, T. Abel and M.L. Norman: yt: A Multi-code Analysis Toolkit for Astrophysical Simulation Data. ApJS 192:9 (2011) (eprint)

• Debian Astronomy Team
• Ole Streicher

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Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics and affords high-performance interactivity across large or streaming datasets. Bokeh can help anyone who wants to create interactive plots, dashboards, and data applications quickly and easily.

• Debian Python Team
• Roland Mas

• 3.4.0-0 (VCS)

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PySpecKit is a code framework designed to allow for analysis of spectroscopic data from a wide variety of astronomical instruments. Initial implementation focuses on optical and radio applications, e.g. gaussian and voigt profile fitting, baseline/continuum fitting, and equivalent width measurements.

• Debian Astro Team
• Vincent Prat

• 0.1.20-1 (VCS)

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Sherpa is a Python package for modeling and fitting. It enables the user to construct complex models from simple definitions and fit those models to data, using a variety of statistics and optimization methods.

It was originally developed by the Smithsonian Astrophysical Observatory / Chandra X-Ray Center as part of the larger CIAO package for X-ray data analysis

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