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PynPoint

Pipeline for processing and analysis of high-contrast imaging data

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PynPoint is a generic, end-to-end pipeline for the reduction and analysis of high-contrast imaging data of exoplanets. The pipeline uses principal component analysis (PCA) for the subtraction of the stellar PSF and supports post-processing with ADI, RDI, and SDI techniques. The package is stable, extensively tested, and actively maintained.

Documentation

Documentation is available at http://pynpoint.readthedocs.io, including installation instructions, details on the pipeline architecture, and several notebook tutorials.

Attribution

If you use PynPoint in your publication then please cite Stolker et al. (2019). Please also cite Amara & Quanz (2012) as the origin of PynPoint, which focused initially on the use of 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 et al. (preprint), respectively.

Contributing

Contributions in the form of bug fixes, new or improved functionalities, and pipeline modules are highly appreciated. Please consider forking the repository and creating a pull request to help improve and extend the package. Instructions for coding of a pipeline module are available in the documentation. Bugs can be reported by creating an issue on the Github page.

License

Copyright 2014-2021 Tomas Stolker, Markus Bonse, Sascha Quanz, Adam Amara, and contributors.

PynPoint is 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 Interactive and is available for use in presentations.

pynpoint's People

Contributors

18alex96 avatar aamara avatar aboehle avatar benassa-de-glassa avatar gcugno avatar jakeret avatar jasperjonker avatar kiefersv avatar markusbonse avatar timothygebhard avatar tomasstolker avatar

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pynpoint's Issues

Implement multi threading on images

Bring the implementation which already exists from the wavelet to the master branch. Adapt the code for line in time multi threading to images.

Checking repo

  • make sure the git repo has everything from the SVN
  • make sure code still works - especially all tests

Get all Port

The get_all() function reads the data as float64 even if it is only float32. Change this or add a parameter and fix unit tests.

Fix the header information Warnings.

1 solution:

  • turn them on in a debug mode (config file)

2 solution:

  • read only a subset (all needed) header keys
  • use the dict mentioned in a "Easy to use" issue

PynPoint Installation on Linux

I wanted to install PynPoint on Linux Ubunt 16.04 LTS and couldn't import it due to the an ImportError. I proceeded as follows:

  • Cloned the repo from GitHub

  • Run (Python 2.7) python2 setup.py install, which run successfully

  • When importing PynPoint there was an ImportError due to the numba package

       OSError: /usr/local/lib/python2.7/dist-packages/llvmlite-0.14.0-py2.7.egg/llvmlite/binding/libllvmlite.so: cannot open shared object file: No such file or directory 
  • Same error occured when trying to import numba

Solution
It was suggested here that one should use conda to handle the environments. I downloaded Anaconda and now I can import both numba and PynPoint without any errors.

Write a config file which contains a dict for Header keywords

Some Header keywords have different names in different datasets but contain the same information. Create a central dict like:
#Header Key# = a global key in the database

Change all modules (reading and processing) which have hard coded Header keywords.

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