Skip to main content

A comprehensive PDI pipeline for NACO data (PIPPIN)

Project description

PIPPIN (PDI pipeline for NACO data) is a pipeline capable of reducing the polarimetric observations made by the VLT/NACO instrument. It applies the Polarimetric Differential Imaging (PDI) technique to separate the polarised, scattered light from the (largely) un-polarised, stellar light. As a result, circumstellar dust can be uncovered.

Documentation can be found at https://pippin-naco.readthedocs.io

A comprehensive archive of the PIPPIN-reduced NACO data products can be found at archive. There, we have published our reductions of potential young systems observed with NACO’s polarimetric configuration paper-citation.

If you use PIPPIN or PIPPIN-reduced data products from the archive for your publication, please cite our paper.

PIPPIN is distributed under the MIT License (for terms and conditions, see LICENSE).

Copyright (C) 2023 Sam de Regt

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

pippin_naco-0.1.1.tar.gz (39.4 kB view details)

Uploaded Source

Built Distribution

pippin_naco-0.1.1-py3-none-any.whl (41.3 kB view details)

Uploaded Python 3

File details

Details for the file pippin_naco-0.1.1.tar.gz.

File metadata

  • Download URL: pippin_naco-0.1.1.tar.gz
  • Upload date:
  • Size: 39.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.12

File hashes

Hashes for pippin_naco-0.1.1.tar.gz
Algorithm Hash digest
SHA256 b15df5486c52bb0f394bcc8bde93489cc22563bf49fe6401edcdf961cff33d2a
MD5 9769814f8974c376441d4e65cd44145e
BLAKE2b-256 d63e29deb3128c48260ce95d4cd3413874382f8b85f3a7b2947c354923cab2eb

See more details on using hashes here.

File details

Details for the file pippin_naco-0.1.1-py3-none-any.whl.

File metadata

  • Download URL: pippin_naco-0.1.1-py3-none-any.whl
  • Upload date:
  • Size: 41.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.12

File hashes

Hashes for pippin_naco-0.1.1-py3-none-any.whl
Algorithm Hash digest
SHA256 b7ebf4505af384c77fa5716e00a1a57e6229c628b688c87e185de66dc231e584
MD5 990d5fef4b1a6a86cc489a24e0897323
BLAKE2b-256 e10c03ecbd6d77b766c0216d9248ac3a8d116654b1248eef5f5120b89be4ccff

See more details on using hashes here.

Supported by

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