Debian Astro Python packages

Python2 je vysokoúrovňový, interaktívny objektovo orientovaný jazyk. Obsahuje vyčerpávajúcu knižnicu tried s množstvom užitočných funkcií pre sieťové programovanie, správu systému, zvuk a grafiku.

Tento balík je závislosť, ktorá závisí od predvolenej verzie Pythonu 2 v Debiane (momentálne v2.7).

• Matthias Klose

• 2.7.16-1 (buster)
• 2.7.13-2 (stretch)
• 2.7.9-1 (jessie)

devel

interpreter, lang:python

interface

commandline

role

metapackage, program

scope

utility

• 1127 regular users
• 471 recent
• 38201 total installations (of 248883 submissions)

• idle

• Maintainer page
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• Home page

• P. Greenfield, M. Droettboom and E. Bray: ASDF: A new data format for astronomy. Astronomy and Computing 12:240-251 (2015) (eprint)

• Debian Astronomy Team
• Ole Streicher

• 1.2.1-2 (stretch)
• 4.2.0 (upstream)

• 1 total installations (of 248883 submissions)

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• D. Lang, D. W. Hogg, K. Mierle, M. Blanton and S. Roweis: Astrometry.net: Blind Astrometric Calibration of Arbitrary Astronomical Images. aj 139:1782-1800 (2010) (eprint)

• Debian Astronomy Team
• Ole Streicher

• 0.70+dfsg-1 (stretch)

• 54 total installations (of 248883 submissions)

• Maintainer page
• Bugs
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• Debian Astro Team
• Vincent Prat

• 0.2-5 (stretch)

• 1 regular users
• 1 total installations (of 248883 submissions)

• Maintainer page
• Bugs
• Git

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• Debian Astronomy Team
• Ole Streicher

• 1.2 (stretch)

• 1 total installations (of 248883 submissions)

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• 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

• 1.0.5-1 (stretch)

• 1 regular users
• 1 total installations (of 248883 submissions)

• Maintainer page
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• Debian Astronomy Team
• Ole Streicher

• 1.2.0-1 (stretch)
• 2.5.1 (upstream)

• 1 regular users
• 1 total installations (of 248883 submissions)

• Maintainer page
• Bugs
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• Gonzaga, S., Hack and W., Fruchter, A. and Mack, J.: The DrizzlePac Handbook. (2012) (eprint)

• Debian Astronomy Team
• Ole Streicher

• 1.3-2 (stretch)
• 2.1.0 (upstream)

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PyEphem provides an ephem Python package for performing high-precision astronomy computations. The underlying numeric routines are coded in C and are the same ones that drive the popular XEphem astronomy application, whose author, Elwood Charles Downey, generously gave permission for their use in PyEphem.

The name ephem is short for the word ephemeris, which is the traditional term for a table giving the position of a planet, asteroid, or comet for a series of dates.

This package contains the Python 2 version of the package.

• Debian Astronomy Team
• Ole Streicher

• 3.7.6.0-6 (stretch)
• 3.7.6.0-7 (buster)

• 4 regular users
• 17 total installations (of 248883 submissions)

• Maintainer page
• Bugs
• Git

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• Debian Astronomy Maintainers
• Josue Ortega

• 1.2-1 (stretch)
• 1.11.0 (upstream)

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• Debian Astronomy Team
• Ole Streicher

• 2.6.1-2 (stretch)

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Gyoto aims at providing a framework for computing orbits and ray-traced images in General relativity.

This package also includes a Gyoto plug-in allowing to write new Gyoto objects in the Python 2 language.

This package provides an extension for the Python 2 programming language exposing the Gyoto facilities. It allows using Gyoto interactively from the Python prompt or running complex Gyoto scripts.

• F. H. Vincent, T. Paumard, E. Gourgoulhon and G. Perrin: GYOTO: a new general relativistic ray-tracing code. Classical and Quantum Gravity 28:225011 (2011) (eprint)

• Debian Astronomy Maintainers
• Thibaut Paumard

• 1.3.1-1 (buster)
• 1.2.0-2 (stretch)

• 8 total installations (of 248883 submissions)

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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 2 version of the package.

• Debian Science Maintainers
• Andreas Tille

• 0.9.11+dfsg-2 (buster)
• 0.9.5+dfsg-2 (stretch)
• 0.8.0+dfsg.1-1 (jessie)

• 16 total installations (of 248883 submissions)

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• Debian Astronomy Team
• Ole Streicher

• 0.9.8-4 (stretch)

• 1 regular users
• 1 total installations (of 248883 submissions)

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Numpy obsahuje mocný objekt N-rozmerného poľa, pokročilé (vysielajúce) funkcie, nástroje na integráciu s kódom v C/C++ a Fortrane a užitočné funkcie lineárnej algebry, fourierovych transformácií a práce s náhodnými číslami.

Numpy nahrádza moduly python-numeric a python-numarray, ktoré sú teraz zavrhované a nemali by sa používať, jedine na podporu staršieho softvéru.

• Sandro Tosi

• 1.12.1-3 (stretch)
• 1.8.2-2 (jessie)
• 1.16.2-1 (buster)

devel

lang:python, library

field

mathematics

role

devel-lib

• 352 regular users
• 41 recent
• 7463 total installations (of 248883 submissions)

• python-shapely

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• Debian Astronomy Team
• Ole Streicher

• 0.3-3 (stretch)

• 1 regular users
• 1 total installations (of 248883 submissions)

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• Debian Astro Team
• Vincent Prat

• 0.5.3-3 (stretch)

• 1 regular users
• 1 total installations (of 248883 submissions)

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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 python-gpyfft, which provides bindings to the OpenCL FFT library clFFT.

This package provides the Python 2 bindings.

• Debian Science Maintainers
• Drew Parsons

• 0.11.1-2 (buster)
• 0.9.2+dfsg-2 (jessie)

• 3 total installations (of 248883 submissions)

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• Debian Astro Team
• Paul Sladen

• 0.4.2-1 (stretch)

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

• Debian Astronomy Team
• Ole Streicher

• 0.9.8.5+dfsg-1 (stretch)

• Maintainer page
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• Debian Astronomy Team
• Ole Streicher

• 0.3.1-4 (stretch)

• 1 regular users
• 1 total installations (of 248883 submissions)

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scikit-image is a collection of image processing algorithms for Python. It performs tasks such as image loading, filtering, morphology, segmentation, color conversions, and transformations.

This package provides the Python 2 module.

• Stéfan van der Walt, Johannes L. Schönberger, Juan Nunez-Iglesias, François Boulogne, Joshua D. Warner, Neil Yager, Emmanuelle Gouillart, Tony Yu and the scikit-image contributors: scikit-image: Image processing in Python. PeerJ 2:e453 (2014) (eprint)

• Debian Science Maintainers
• Ole Streicher

• 0.12.3-8 (stretch)
• 0.14.2-2 (buster)
• 0.10.1-2 (jessie)

• 3 regular users
• 38 total installations (of 248883 submissions)

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• A. Ginsburg, T. Robitaille, C. Beaumont, E. Rosolowsky, A. Leroy, C. Brogan, T. Hunter, P. Teuben and D. Brisbin: Radio Astronomy Tools in Python: Spectral-cube, pvextractor, and more. Revolution in Astronomy with ALMA: The Third Year :363 (2015) (eprint)

• Debian Astronomy Team
• Ole Streicher

• 0.4.0-2 (stretch)

• 1 regular users
• 1 total installations (of 248883 submissions)

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• T. SunPy Community, S.J. Mumford, S. Christe, D. Perez-Suarez, J. Ireland, A.Y. Shih, A.R. Inglis, S. Liedtke, R.J. Hewett, F. Mayer, K. Hughitt, N. Freij, T. Meszaros, S.M. Bennett, M. Malocha, J. Evans, A. Agrawal, A.J. Leonard, T.P. Robitaille, B. Mampaey, J. Ivan Campos-Rozo and M.S. Kirk: SunPy-Python for solar physics. Computational Science and Discovery 8(1):014009 (2015) (eprint)

• Debian Astronomy Team
• Ole Streicher

• 0.7.4-2 (stretch)
• 7.0.0 (upstream)

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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 2 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
• BW Keller

• 3.5.0-1 (buster)
• 3.2.3-2 (stretch)
• 4.4.1 (upstream)

• 1 regular users
• 9 total installations (of 248883 submissions)

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The VOTable format is defined by the International Virtual Observatory Alliance to facilitate data exchange in astronomy. This library implements most of the specification and offers simple and advanced, streaming interfaces. Also included is a simple client to query TAP servers.

There are many other libraries to parse VOTables into python programs, the default probably being astropy's. This one is geared for maximal control, streaming, and the possibility to write fairly mad instance documents.

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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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