Tento metabalík nainštaluje balíky jazyka Python 2 pre astronómiu.
Tieto balíky je možné využiť na interaktívnu analýzu alebo na tvorbu
špecifických programov.
Pamätajte, že Python 2 sa udržiava v režime podpory spätnej kompatibility.
Python 3 je súčasnosť a budúcnosť jazyka. Podpora jazyka Python 2 skončí v
roku 2020. Prosím, použite Python 2 iba ak nemáte inú možnosť.
Official Debian packages with high relevance
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ipython
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5.1.0-3
↺
New upstream version
8.22.2
available
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??? missing short description for package ipython :-(
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ipython:
??? missing short description for package ipython :-(
- Maintainer
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✏
- devel
- interpreter, lang:python
- interface
- shell
- role
- program
- scope
- utility
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- 43 regular users
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4 recent
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990 total installations
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python
|
2.7.9-1
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interaktívny vysokoúrovňový objektovo orientovaný jazyk (verzia python2)
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python:
interaktívny vysokoúrovňový objektovo orientovaný jazyk (verzia python2)
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✏
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).
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- devel
- interpreter, lang:python
- interface
- commandline
- role
- metapackage, program
- scope
- utility
- Popularity
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- 1855 regular users
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2819 recent
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58990 total installations
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python-aplpy
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1.1.1-1
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Astronomical Plotting Library in Python 2
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python-aplpy:
Astronomical Plotting Library in Python 2
- Description
-
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.
This package contains the Python 2 version of the package.
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- 1 regular users
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2 total installations
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python-asdf
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1.2.1-2
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Python library for the Advanced Scientific Data Format
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python-asdf:
Python library for the Advanced Scientific Data Format
- Description
-
ASDF (Advanced Scientific Data Format) is a proposed
next generation interchange format for scientific data. ASDF aims to
exist in the same middle ground that made FITS so successful, by
being a hybrid text and binary format: containing human editable
metadata for interchange, and raw binary data that is fast to load
and use. Unlike FITS, the metadata is highly structured and is
designed up-front for extensibility.
This is the Python 2 package.
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python-astlib
|
0.8.0-4
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všeobecné nástroje jazyka Python na astronómiu
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python-astlib:
všeobecné nástroje jazyka Python na astronómiu
- Description
-
✏
astLib je sada modulov jazyka Python poskytujúcich niektoré nástroje astronómom na výskum. Je možné použiť ju na astronomické výkresy,
štatistiku, bežné výpočty konverzie súradníc a prácu s obrázkami FITS s
informáciami World Coordinate System (WCS) prostredníctvom PyWCSTools -
jednoduchého wrappera WCSTools, ktorý napísala Jessica Mink. PyWCSTools sú
šírené (a vyvíjané) ako súčasť astLib.
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python-astrometry
|
0.70+dfsg-1
↺
New upstream version
0.94
available
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Astrometry plate solver (Python package)
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python-astrometry:
Astrometry plate solver (Python package)
- Description
-
The astrometry engine will take any image and return the astrometry
world coordinate system (WCS), a standards-based description of the
transformation between image coordinates and sky coordinates.
This package contains the Python Package.
- Reference
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- Maintainer
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-
-
0.70+dfsg-1
(stretch)
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0.94
(upstream)
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122 total installations
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python-astroml
|
0.3-6
↺
New upstream version
1.0.2.post1
available
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Python Machine Learning library for astronomy
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python-astroml:
Python Machine Learning library for astronomy
- Description
-
AstroML is a Python module for machine learning and data mining built on
numpy, scipy, scikit-learn, and matplotlib. It contains a growing library of
statistical and machine learning routines for analyzing astronomical data in
Python, loaders for several open astronomical datasets, and a large suite of
examples of analyzing and visualizing astronomical datasets.
- Reference
-
- Maintainer
-
- Versions
-
-
0.3-6
(stretch)
-
1.0.2.post1
(upstream)
- Popularity
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- 1 regular users
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2 total installations
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python-astroplan
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0.2-5
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Observation planning package for astronomers (Python 2)
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python-astroplan:
Observation planning package for astronomers (Python 2)
- Description
-
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 2 version of the package.
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- Popularity
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- 1 regular users
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1 total installations
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python-astropy
|
1.3-8
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základné funkcie na astrofyziku v jazyku Python
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python-astropy:
základné funkcie na astrofyziku v jazyku Python
- Description
-
✏
Balík astropy obsahuje základné funkcie a niekoré bežné nástroje na výskum v
oblasti astronómie a astrofyziky v jazyku Python.
Je možné ho rozšíriť o niekoľko „súvisiacich balíkov“, ktoré spolupracujú so
základným balíkom.
- Reference
-
-
Astropy Collaboration and Robitaille, T.P. and Tollerud, E.J. and Greenfield, P. and Droettboom, M. and Bray, E. and Aldcroft, T. and Davis, M. and Ginsburg, A. and Price-Whelan, A.M. and Kerzendorf, W.E. and Conley, A. and Crighton, N. and Barbary, K. and Muna, D. and Ferguson, H. and Grollier, F. and Parikh, M.M. and Nair, P.H. and Unther, H.M. and Deil, C. and Woillez, J. and Conseil, S. and Kramer, R. and Turner, J.E.H. and Singer, L. and Fox, R. and Weaver, B.A. and Zabalza, V. and Edwards, Z.I. and Azalee Bostroem, K. and Burke, D.J. and Casey, A.R. and Crawford, S.M. and Dencheva, N. and Ely, J. and Jenness, T. and Labrie, K. and Lim, P.L. and Pierfederici, F. and Pontzen, A. and Ptak, A. and Refsdal, B. and Servillat, M. and Streicher, O.:
Astropy: A community Python package for astronomy.
Astronomy & Astrophysics
558(A33):A33
(2013)
(eprint)
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- 1 regular users
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151 total installations
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python-astropy-affiliated
|
1.2
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Collection of all astropy affiliated packages (Python 2)
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python-astropy-affiliated:
Collection of all astropy affiliated packages (Python 2)
- Description
-
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 in their Python 2 version.
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1 total installations
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python-astroquery
|
0.3.4+dfsg-3
↺
New upstream version
0.4.7
available
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Python online astronomical database querying (Python 2)
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python-astroquery:
Python online astronomical database querying (Python 2)
- Description
-
Astroquery is a set of Python tools for querying astronomical web forms and
databases.
This is the Python 2 version of the package.
- Maintainer
-
- Versions
-
-
0.3.4+dfsg-3
(stretch)
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0.4.7
(upstream)
- Popularity
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1 total installations
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python-astroscrappy
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1.0.5-1
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Optimized Cosmic Ray Annihilation in Python 2
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python-astroscrappy:
Optimized Cosmic Ray Annihilation in Python 2
- Description
-
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.
This package contains the Python 2 version of the package.
- Reference
-
- Maintainer
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- Versions
-
- Popularity
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- 1 regular users
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2 total installations
(of 236867 submissions)
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python-casacore
|
2.1.2-3
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Python 2 bindings to the casacore library
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python-casacore:
Python 2 bindings to the casacore library
- Description
-
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 2 wrapper around the library.
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python-ccdproc
|
1.2.0-1
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Basic data reductions of astronomy CCD images (Python 2)
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python-ccdproc:
Basic data reductions of astronomy CCD images (Python 2)
- Description
-
The ccdproc package provides many of the necessary tools for processing
of ccd images built on a framework to provide error propagation and bad
pixel tracking throughout the reduction process.
This package contains the Python 2 version of the package.
- Maintainer
-
- Versions
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- Popularity
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- 1 regular users
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2 total installations
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python-cpl
|
0.6.2-4
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Control pipeline recipes from the European Southern Observatory
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python-cpl:
Control pipeline recipes from the European Southern Observatory
- Description
-
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 pyfits 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.
- Reference
-
- Maintainer
-
- Versions
-
- Tags
-
✏
- devel
- lang:python, library
- field
- astronomy
- role
- devel-lib
- science
- calculation
- use
- converting, organizing
- works-with
- image, image:raster
- Popularity
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- 1 regular users
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29 total installations
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python-drizzle
|
1.3-2
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Dithered image combination for Python 2
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python-drizzle:
Dithered image combination for Python 2
- Description
-
The drizzle library is a Python package for combining dithered images
into a single image. This library is derived from code used in
drizzlepac. Like drizzlepac, most of the code is implemented in the C
language. The biggest change from drizzlepac is that this code passes
an array that maps the input to output image into the C code, while
the drizzlepac code computes the mapping by using a Python
callback. Switching to using an array allowed the code to be greatly
simplified.
This is the Python 2 package.
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python-emcee
|
2.2.1-1
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Affine-invariant ensemble MCMC sampling for Python
|
python-emcee:
Affine-invariant ensemble MCMC sampling for Python
- Description
-
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.
This is the Python 2 package.
- Reference
-
-
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)
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- 2 regular users
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5 total installations
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python-ephem
|
3.7.6.0-7
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Compute positions of the planets and stars with Python 2
|
python-ephem:
Compute positions of the planets and stars with Python 2
- Description
-
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.
- Maintainer
-
- Versions
-
- Popularity
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- 7 regular users
-
27 total installations
(of 236867 submissions)
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python-fitsio
|
0.9.10+dfsg-1
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Python 2 library to read from and write to FITS files
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python-fitsio:
Python 2 library to read from and write to FITS files
- Description
-
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).
This is the Python 2 package.
- Maintainer
-
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- Popularity
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- 1 regular users
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8 total installations
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python-galpy
|
1.2-1
↺
New upstream version
1.9.2
available
|
Python 2 package for galactic dynamics
|
python-galpy:
Python 2 package for galactic dynamics
- Description
-
Python module that supports orbit integration in a variety of potentials,
evaluating and sampling various distribution functions, and the calculation of
action-angle coordinates for all static potentials.
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python-gdl
|
0.9.7-2
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Python interface for the GNU Data Language
|
python-gdl:
Python interface for the GNU Data Language
- Description
-
GNU Data Language (GDL) is a free IDL (Interactive Data Language)
compatible incremental compiler. It has full syntax compatibility
with IDL 7.1. IDL is a registered trademark of ITT Visual Information
Solutions. From IDL 8.0, the following language elements are
supported:
- FOREACH loop
- negative array indices
- garbage collection pointers and objects
- call methods on an object using "." (e. g.
object.aMemberProcedure,arg1 )
The file input output system is fully implemented. GUI support (widgets) is
officially provided, but it's not complete yet.
This package contains the Python library/interface.
- Reference
-
-
A. Coulais, M. Schellens, J. Gales, S. Arabas, M. Boquien, P. Chanial, P. Messmer, D. Fillmore, O. Poplawski, S. Maret, G. Marchal, N. Galmiche and T. Mermet:
Status of GDL - GNU Data Language.
Astronomical Data Analysis Software and Systems XIX
434:187
(2010)
(eprint)
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9 total installations
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python-ginga
|
2.6.1-2
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Astronomical image toolkit for Python 2
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python-ginga:
Astronomical image toolkit for Python 2
-
-
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.
This package contains the Python 2 version of the package.
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python-glue
|
0.9.1+dfsg-1
↺
New upstream version
1.18.0
available
|
Python 2 library for data interaction
|
python-glue:
Python 2 library for data interaction
- Description
-
python-glue is a Python library for data interaction, it blurs the boundary
between GUI-centric and code-centric data exploration.
There are many ways to leverage Glue from Python. Among other things, you can
write code to do the following:
- Send data in the form of NumPy arrays or Pandas DataFrames to Glue for
exploration.
- Write startup scripts that automatically load and clean data,
before starting Glue.
- Write custom functions to parse files, and plug these functions into the
Glue GUI.
- Write custom functions to link datasets, and plug these into the Glue GUI.
- Create your own visualization modules.
- Reference
-
- Maintainer
-
- Versions
-
-
0.9.1+dfsg-1
(stretch)
-
1.18.0
(upstream)
- Debian
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python-gyoto
|
1.2.0-2
|
General relativistic geodesic integration for the Python 2 language
|
python-gyoto:
General relativistic geodesic integration for the Python 2 language
- Description
-
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.
- Reference
-
- Maintainer
-
- Versions
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- Popularity
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26 total installations
(of 236867 submissions)
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python-healpy
|
1.10.3-2
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HEALPix representation of spherical data - Python interface
|
python-healpy:
HEALPix representation of spherical data - Python interface
- Description
-
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 Python wrapper around the C++ implementation of
HEALPix.
- Maintainer
-
- Versions
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- Popularity
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- 1 regular users
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3 total installations
(of 236867 submissions)
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python-lmfit
|
0.8.0+dfsg.1-1
|
Least-Squares Minimization with Constraints (Python 2)
|
python-lmfit:
Least-Squares Minimization with Constraints (Python 2)
- Description
-
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.
- Maintainer
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35 total installations
(of 236867 submissions)
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python-matplotlib
|
2.2.3-6
|
systém v Pythone na vykresľovanie v štýle podobnom Matlabu
|
python-matplotlib:
systém v Pythone na vykresľovanie v štýle podobnom Matlabu
- Description
-
✏
Matplotlib je knižnica v čistom Pythone s cieľom priniesť do Pythonu
vykresľovanie v publikačnej kvalite so syntaxou známou používateľom
Matlabu.
Ku všetkým vykresľovacím príkazom v rozhraní pylab je možné pristupovať buď
prostredníctvom funkcionálneho rozhrania známeho používateľom Matlabu alebo
prostredníctvom objektovo orientovaného rozhrania známeho používateľom
Pythonu.
- Maintainer
-
- Versions
-
- Tags
-
✏
- devel
- interpreter, lang:python, library
- field
- mathematics, physics
- interface
- shell, x11
- role
- devel-lib, program
- scope
- application
- uitoolkit
- gtk, qt, tk, wxwidgets
- use
- analysing, viewing
- works-with
- image, image:raster, image:vector
- works-with-format
- jpg, pdf, png, postscript, svg, tex
- x11
- application
- Popularity
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- 1057 regular users
-
2 recent
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1939 total installations
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python-montage-wrapper
|
0.9.8-4
|
Python-2 wrapper for the Montage mosaicking toolkit
|
python-montage-wrapper:
Python-2 wrapper for the Montage mosaicking toolkit
- Description
-
This package provides a Python-2 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.
- Maintainer
-
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- 1 regular users
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2 total installations
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python-mvpa2
|
2.3.1-2
|
multivariate pattern analysis with Python v. 2
|
python-mvpa2:
multivariate pattern analysis with Python v. 2
- Description
-
PyMVPA eases pattern classification analyses of large datasets, with an
accent on neuroimaging. It provides high-level abstraction of typical
processing steps (e.g. data preparation, classification, feature selection,
generalization testing), a number of implementations of some popular
algorithms (e.g. kNN, Ridge Regressions, Sparse Multinomial Logistic
Regression), and bindings to external machine learning libraries (libsvm,
shogun).
While it is not limited to neuroimaging data (e.g. fMRI, or EEG) it
is eminently suited for such datasets.
This is a package of PyMVPA v.2. Previously released stable version
is provided by the python-mvpa package.
- Maintainer
-
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- 6 regular users
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74 total installations
(of 236867 submissions)
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python-numpy
|
1.16.2-1
|
Numerical Python pridáva schopnosti rýchlych polí do jazyka Python
|
python-numpy:
Numerical Python pridáva schopnosti rýchlych polí do jazyka Python
- Description
-
✏
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.
- Maintainer
-
- Versions
-
- Tags
-
✏
- devel
- lang:python, library
- field
- mathematics
- role
- devel-lib
- Popularity
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- 651 regular users
-
139 recent
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13018 total installations
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python-pandas
|
0.23.3+dfsg-3
↺
New upstream version
2.2.1
available
|
údajové štruktúry „relačných“ dát alebo dát „s označením“
|
python-pandas:
údajové štruktúry „relačných“ dát alebo dát „s označením“
- Description
-
✏
pandas balík jazyka Python poskytujúci rýchle, flexibilné a expresívne
údajové štruktúry navrhnuté na zjednodušenie a zintuitívnenie práce s
„relačnými“ dátami a dátami „s označením“. Jeho cieľom je byť základným
stavebným blokom praktickej analýzy dát skutočného sveta v jazyku Python.
pandas sa hodí na rôzne druhy dát:
- Tabuľkové dáta so stĺpcami heterogénnych typov, ako v tabuľke SQL
alebo hárku tabuľkového kalkulátora.
- Zoradené a nezoradené dáta časových radov (nie nutne s pevnou
frekvenciou).
- Ľubovoľné maticové dáta (homogénneho alebo heterogénneho typu)
s označením riadkov a stĺpcov.
- Akékoľvek iné sady experimentálnych alebo štatistických dát. Dáta
v skutočnosti nemusia byť označené, aby ste ich mohli umiestniť do
údajovej štruktúry pandas.
Tento balík obsahuje verziu pre Python 2.
- Reference
-
-
McKinney, Wes:
pandas: a Foundational Python Library for Data Analysis and Statistics.
(2011)
(eprint)
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- 9 regular users
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1 recent
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462 total installations
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python-photutils
|
0.3-3
|
Astropy affiliated package for image photometry
|
python-photutils:
Astropy affiliated package for image photometry
- Description
-
Photutils contains functions for:
- estimating the background and background rms in astronomical images
- detecting sources in astronomical images
- estimating morphological parameters of those sources (e.g., centroid
and shape parameters)
- performing aperture and PSF photometry
This package contains the Python 2 version of the package.
- Maintainer
-
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- Popularity
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- 1 regular users
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1 total installations
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python-pyavm
|
0.9.1-1
|
Python module to handle Astronomy Visualization Metadata Standard
|
python-pyavm:
Python module to handle Astronomy Visualization Metadata Standard
- Description
-
PyAVM is a module to represent, read, and write metadata following the
Astronomy Visualization Metadata (AVM) standard.
With pyavm you can:
-Parse AVM meta-data from an existing image.
-Access and Set the metadata.
-Create an AVM object from scratch.
-Convert to a WCS object.
-Initialize from a FITS header.
-Initialize from a WCS object.
- Maintainer
-
- Versions
-
- Popularity
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- 1 regular users
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2 total installations
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python-pydl
|
0.5.3-3
|
Library of IDL astronomy routines converted to Python 2
|
python-pydl:
Library of IDL astronomy routines converted to Python 2
- Description
-
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 2 version of the package.
- Maintainer
-
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- 1 regular users
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1 total installations
(of 236867 submissions)
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python-pyds9
|
1.8.1-1
|
Communication with the ds9 image display program (Python-2)
|
python-pyds9:
Communication with the ds9 image display program (Python-2)
- Description
-
The XPA messaging system provides seamless communication between many kinds
of Unix programs, including Tcl/Tk programs such as ds9. The pyds9 module
uses a Python interface to XPA to communicate with ds9. It supports
communication with all of ds9’s XPA access points.
This package provides the Python-2 version of the package.
- Maintainer
-
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- Popularity
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- 1 regular users
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2 total installations
(of 236867 submissions)
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python-pyfftw
|
0.9.2+dfsg-2
|
Pythonic wrapper around FFTW - Python 2
|
python-pyfftw:
Pythonic wrapper around FFTW - Python 2
- Description
-
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.
- Maintainer
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- Popularity
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8 total installations
(of 236867 submissions)
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python-pymc
|
2.2+ds-1
|
Bayesian statistical models and fitting algorithms
|
python-pymc:
Bayesian statistical models and fitting algorithms
- Description
-
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.
- Maintainer
-
- Versions
-
- Popularity
-
- 2 regular users
-
26 total installations
(of 236867 submissions)
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python-pymoc
|
0.4.2-1
|
Python 2 Multi-Order Coverage maps for Virtual Observatory
|
python-pymoc:
Python 2 Multi-Order Coverage maps for Virtual Observatory
- Description
-
PyMOC provides a Python 2-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.
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python-pyregion
|
1.2-4
|
Python module to parse ds9 region files (Python 2)
|
python-pyregion:
Python module to parse ds9 region files (Python 2)
- Description
-
Pyregion is a Python module to parse ds9 region files. It also supports ciao
region files.
This is the Python 2 version of the package.
- Maintainer
-
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- 1 regular users
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2 total installations
(of 236867 submissions)
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python-pysynphot
|
0.9.8.5+dfsg-1
|
Python Synthetic Photometry Utilities
|
python-pysynphot:
Python Synthetic Photometry Utilities
- Description
-
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.
- Reference
-
-
P. L. Lim, R. I. Diaz and V. Laidler:
PySynphot User’s Guide.
(2015)
- Maintainer
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python-pyvo
|
0.5.0+dfsg-1
↺
New upstream version
1.5.1
available
|
Python library for data services of the Virtual observatory (VO)
|
python-pyvo:
Python library for data services of the Virtual observatory (VO)
- Description
-
The pyvo module currently provides these main capabilities:
Find archives that provide particular data of a particular type and/or relates
to a particular topic search an archive for datasets of a particular type.
Do simple searches on catalogs or databases get information about an object via
its name.
This is the Python 2 version of the package.
- Maintainer
-
- Versions
-
-
0.5.0+dfsg-1
(stretch)
-
1.5.1
(upstream)
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python-reproject
|
0.3.1-4
|
Reproject astronomical images with Python 2
|
python-reproject:
Reproject astronomical images with Python 2
- Description
-
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 2 version of the package.
- Maintainer
-
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-
- Popularity
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- 1 regular users
-
2 total installations
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python-scipy
|
0.18.1-2
|
vedecké nástroje pre Python
|
python-scipy:
vedecké nástroje pre Python
- Description
-
✏
SciPy dopĺňa populárny modul NumPy (balík python-numpy) a v rámci jedného
balíka poskytuje rozličné vysokoúrovňové vedecké a inžinierske moduly.
SciPy je sada open source vedeckých a numerických nástrojov pre Python.
Momentálne podporuje špeciálne funkcie, integrály, riešenie obyčajných
diferenciálnych rovníc (ODR), optimalizáciu gradientov, genetické algoritmy,
nástroje na paralelné programovanie, kompilátor výrazov do C++ na rýchly beh
a ďalšie.
- Maintainer
-
- Versions
-
- Tags
-
✏
- devel
- lang:python, library
- role
- devel-lib
- Popularity
-
- 59 regular users
-
2 recent
-
1117 total installations
(of 236867 submissions)
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python-skimage
|
0.10.1-2
|
Python modules for image processing
|
python-skimage:
Python modules for image processing
- Description
-
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.
- Reference
-
-
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)
- Maintainer
-
- Versions
-
- Popularity
-
- 4 regular users
-
65 total installations
(of 236867 submissions)
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python-sklearn
|
0.14.1-3
|
Python modules for machine learning and data mining - Python 2
|
python-sklearn:
Python modules for machine learning and data mining - Python 2
- Description
-
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)
- Maintainer
-
- Versions
-
- Popularity
-
- 19 regular users
-
1 recent
-
204 total installations
(of 236867 submissions)
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python-spectral-cube
|
0.4.0-2
|
Manipulate astronomical data cubes with Python 2
|
python-spectral-cube:
Manipulate astronomical data cubes with Python 2
- Description
-
The spectral-cube package 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 aims to be a
versatile data container for building custom analysis routines.
This package contains the Python 2 version of the package.
- Reference
-
- Maintainer
-
- Versions
-
- Popularity
-
- 1 regular users
-
2 total installations
(of 236867 submissions)
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python-specutils
|
0.2.2-1
|
Base classes and utilities for astronomical spectra in Python 2
|
python-specutils:
Base classes and utilities for astronomical spectra in Python 2
- Description
-
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.
This package contains the Python 2 version of the package.
- Maintainer
-
- Versions
-
- Popularity
-
- 1 regular users
-
1 total installations
(of 236867 submissions)
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python-sunpy
|
0.7.4-2
|
Software library for solar physics based on Python
|
python-sunpy:
Software library for solar physics based on Python
- Description
-
SunPy is a community-developed free and open-source software package for
solar physics. SunPy is meant to be a free alternative to the SolarSoft data
analysis environment. The aim of the SunPy project is to provide the
software tools necessary so that anyone can analyze solar data.
This package contains the Python 2 version.
- Reference
-
-
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)
- Maintainer
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python-vispy
|
0.4.0-1
↺
New upstream version
0.14.2
available
|
interactive visualization in Python 2
|
python-vispy:
interactive visualization in Python 2
- Description
-
Vispy is a high-performance interactive 2D/3D data visualization library.
Vispy leverages the computational power of modern Graphics Processing Units
(GPUs) through the OpenGL library to display very large datasets.
This package provides VisPy for the Python 2 interpreter.
- Reference
-
- Maintainer
-
- Versions
-
- Popularity
-
-
1 total installations
(of 236867 submissions)
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python-yt
|
3.2.3-2
|
Framework for analyzing and visualizing simulation data (Python 2)
|
python-yt:
Framework for analyzing and visualizing simulation data (Python 2)
- Description
-
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.
- Reference
-
- Maintainer
-
- Versions
-
- Popularity
-
- 1 regular users
-
8 total installations
(of 236867 submissions)
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No known packages available but some record of interest (WNPP bug)
|
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|
python-extinction
|
|
Fast interstellar dust extinction laws
|
python-extinction:
Fast interstellar dust extinction laws
- Description
-
Extinction provides a library with Cython-optimised implementations of
empirical dust extinction laws found in the astronomical literature.
- Debian
-
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python-gavovotable
|
|
A library to process VOTables using python
|
python-gavovotable:
A library to process VOTables using python
- Description
-
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.
- Debian
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python-pyspeckit
|
|
Toolkit for fitting and manipulating spectroscopic data
|
python-pyspeckit:
Toolkit for fitting and manipulating spectroscopic data
- Description
-
This is a code framework designed to allow for analysis of
spectroscopic data from a wide variety of astronomical instruments.
It is motivated by the lack of general spectroscopic analysis tools
applicable at multiple wavelengths (compare to IRAF, SPLAT, etc. -
these are wavelength-specific and/or do not make user scripting
easy). Initial implementation focuses on optical and radio
applications, e.g. gaussian and voigt profile fitting,
baseline/continuum fitting, and equivalent width measurements.
- Debian
-
- Upstream
-
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▾
|
python-sherpa
|
|
Modeling and fitting in Python
|
python-sherpa:
Modeling and fitting in Python
- Description
-
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
- Debian
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python-sncosmo
|
|
Python library for high-level supervova cosmology analysis
|
python-sncosmo:
Python library for high-level supervova cosmology analysis
- Description
-
SNCosmo is a Python library for high-level supernova cosmology analysis. It
aims to make such analysis both as flexible and clear as possible. It is
built on NumPy, SciPy and AstroPy. Package Features:
- SN models: Synthesize supernova spectra and photometry from SN models.
- Fitting and sampling: Functions for fitting and sampling SN model
parameters given photometric light curve data.
- Dust laws: Fast implementations of several commonly used extinction laws;
can be used to construct SN models that include dust.
- I/O: Convenience functions for reading and writing peculiar data formats
used in other packages and getting dust values from SFD (1998) maps.
- Built-in 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.
- Extensible: New models, bandpasses, and magnitude systems can be defined,
using an object-oriented interface.
- Debian
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- Upstream
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