Skip to main content

Python package for processing and analysis of high-contrast imaging data

Project description

Python package for processing and analysis of high-contrast imaging data

https://img.shields.io/badge/GitHub-PynPoint-blue.svg?style=flat https://travis-ci.org/PynPoint/PynPoint.svg?branch=master https://codecov.io/gh/PynPoint/PynPoint/branch/master/graph/badge.svg https://www.codefactor.io/repository/github/pynpoint/pynpoint/badge https://readthedocs.org/projects/pynpoint/badge/?version=latest https://img.shields.io/badge/Python-2.7-yellow.svg?style=flat https://img.shields.io/badge/License-GPLv3-blue.svg http://img.shields.io/badge/arXiv-1207.6637-orange.svg?style=flat

PynPoint is an end-to-end pipeline for the data reduction of high-contrast imaging data of planetary and substellar companions, as well as circumstellar disks in scattered light.

The pipeline has a modular architecture with a central data storage in which the reduction steps are stored by the processing modules. These modules have specific tasks such as the subtraction of the background, frame selection, centering, PSF subtraction, and photometric and astrometric measurements. The tags from the central data storage can be written to FITS, HDF5, and text files with the available IO modules.

PynPoint is under continuous development and the latest implementations can be pulled from Github repository. Bug reports, requests for new features, and contributions in the form of new processing modules are highly appreciated. Instructions for writing of modules are provided in the documentation. Bug reports and functionality requests can be provided by creating an issue on the Github page.

Documentation

Documentation can be found at http://pynpoint.readthedocs.io, including installation instructions, details on the architecture of PynPoint, end-to-end examples for data obtained with dithering and nodding, and a description of the various processing modules and parameters.

Attribution

If you use the new architecture of PynPoint (v0.3.0 or later) in your publication then please cite Stolker et al. in prep. Please also cite Amara & Quanz (2012) as the origin of PynPoint, which focused initially on the use of principal component analysis (PCA) as a PSF subtraction method. In case you use specifically the PCA-based background subtraction module or the wavelet based speckle suppression module, please give credit to Hunziker et al. (2018) or Bonse, Quanz & Amara (2018), respectively.

License

Copyright 2014-2018 Tomas Stolker, Markus Bonse, Sascha Quanz, Adam Amara, and contributors.

PynPoint is free software and distributed under the GNU General Public License v3. See the LICENSE file for the terms and conditions.

Acknowledgements

The PynPoint logo was designed by Atlas Infographics.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

PynPoint-0.5.1.tar.gz (659.2 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

PynPoint-0.5.1-py2-none-any.whl (108.1 kB view details)

Uploaded Python 2

File details

Details for the file PynPoint-0.5.1.tar.gz.

File metadata

  • Download URL: PynPoint-0.5.1.tar.gz
  • Upload date:
  • Size: 659.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for PynPoint-0.5.1.tar.gz
Algorithm Hash digest
SHA256 438acf0ee54f757800cbf15797ffa918eb12376d4c065539fb8f43c982c33b52
MD5 eeb8bba3d23ccdffe346746f4973aec4
BLAKE2b-256 240bddbfd730e3950173467480ffdfb90ca75aebaa1440635b1aad924e26153f

See more details on using hashes here.

File details

Details for the file PynPoint-0.5.1-py2-none-any.whl.

File metadata

File hashes

Hashes for PynPoint-0.5.1-py2-none-any.whl
Algorithm Hash digest
SHA256 fb13449e149377d02a70aff8a3d6ca7dd293cc5e6409bc26366bb0a9c8adc5d2
MD5 7e29042b85292116ba2196c6b6d4b423
BLAKE2b-256 de7fd5cf7bf813747dcb419d9a4745a72779c82fc114486696b0c94391f240ab

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page