Skip to main content

No project description provided

Project description

DOI PyPI GitHub Workflow Status

rail_delight

Delight-based stages for RAIL.

This is a hybrid approach that uses both the physics of SEDs and machine learning to produce photometric redshift estimates.

Note when installing, it may fail to build delight. You may need to build delight by hand.

RAIL: Redshift Assessment Infrastructure Layers

RAIL is a flexible software library providing tools to produce at-scale photometric redshift data products, including uncertainties and summary statistics, and stress-test them under realistically complex systematics. A detailed description of RAIL's modular structure is available in the Overview on ReadTheDocs.

RAIL serves as the infrastructure supporting many extragalactic applications of the Legacy Survey of Space and Time (LSST) on the Vera C. Rubin Observatory, including Rubin-wide commissioning activities. RAIL was initiated by the Photometric Redshifts (PZ) Working Group (WG) of the LSST Dark Energy Science Collaboration (DESC) as a result of the lessons learned from the Data Challenge 1 (DC1) experiment to enable the PZ WG Deliverables in the LSST-DESC Science Roadmap (see Sec. 5.18), aiming to guide the selection and implementation of redshift estimators in DESC analysis pipelines. RAIL is developed and maintained by a diverse team comprising DESC Pipeline Scientists (PSs), international in-kind contributors, LSST Interdisciplinary Collaboration for Computing (LINCC) Frameworks software engineers, and other volunteers, but all are welcome to join the team regardless of LSST data rights.

Installation

Installation instructions are available under Installation on ReadTheDocs.

Contributing

The greatest strength of RAIL is its extensibility; those interested in contributing to RAIL should start by consulting the Contributing guidelines on ReadTheDocs.

Citing RAIL

RAIL is open source and may be used according to the terms of its LICENSE (BSD 3-Clause). If you make use of the ideas or software here in any publication, you must cite this repository https://github.com/LSSTDESC/RAIL as "LSST-DESC PZ WG (in prep)" with the Zenodo DOI. Please consider also inviting the developers as co-authors on publications resulting from your use of RAIL by making an issue. Additionally, several of the codes accessible through the RAIL ecosystem must be cited if used in a publication. A convenient list of what to cite may be found under Citing RAIL on ReadTheDocs.

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

pz_rail_delight-1.1.0.tar.gz (566.9 kB view details)

Uploaded Source

Built Distribution

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

pz_rail_delight-1.1.0-py3-none-any.whl (551.9 kB view details)

Uploaded Python 3

File details

Details for the file pz_rail_delight-1.1.0.tar.gz.

File metadata

  • Download URL: pz_rail_delight-1.1.0.tar.gz
  • Upload date:
  • Size: 566.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.12.8

File hashes

Hashes for pz_rail_delight-1.1.0.tar.gz
Algorithm Hash digest
SHA256 cab6acc15267c7f262b6579be30007d73d969949cda7a3b8142cedc179198a6a
MD5 3fc7c7033c1dcc03f6d635b1cfff9118
BLAKE2b-256 5106d60e81b9e1c4f2a456f9d74beaad4f44b6379b44b6f78adc0a5930e09121

See more details on using hashes here.

Provenance

The following attestation bundles were made for pz_rail_delight-1.1.0.tar.gz:

Publisher: publish-to-pypi.yml on LSSTDESC/rail_delight

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file pz_rail_delight-1.1.0-py3-none-any.whl.

File metadata

  • Download URL: pz_rail_delight-1.1.0-py3-none-any.whl
  • Upload date:
  • Size: 551.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.12.8

File hashes

Hashes for pz_rail_delight-1.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 1d4f3d9e63a6411a84912a0c4a6fef0d27a7232a0b7b8f882f654f3d735fb929
MD5 282d72beae254ce39f3222b71ca78728
BLAKE2b-256 b4db4a998f8607c9602b3ee9e42cc9049232a682b15ded16497abdcf9093101a

See more details on using hashes here.

Provenance

The following attestation bundles were made for pz_rail_delight-1.1.0-py3-none-any.whl:

Publisher: publish-to-pypi.yml on LSSTDESC/rail_delight

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

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