Skip to main content

Tools for producing sweights using classic methods or custom orthogonal weight functions (COWs) and for correcting covariance matrices for weighted data fits.

Project description

sweights

pip install sweights

We provide several tools for projecting component weights sWeights in a control variable(s) using a discriminating variable(s). What we call sWeights is the traditional sPlot method (we think that sPlot is a misnomer and hence call it sWeights), but also the new Custom Orthogonal Weight functions (COWs). If you use this package, please cite our methods as:

Dembinski, H., Kenzie, M., Langenbruch, C. and Schmelling, M., Custom Orthogonal Weight functions (COWs) for event classification, NIMA 1040 (2022) 167270

If you cannot access this paper for free, checkout the preprint, arXiv:2112.04574.

We also provide tools for correcting the covariance matrix of fits to weighted data, described in section IV of our paper and in more detail in Langenbruch, arXiv:1911.01303.

Documentation

You can find our documentation here.

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

sweights-1.2.0.tar.gz (619.8 kB view details)

Uploaded Source

Built Distribution

sweights-1.2.0-py3-none-any.whl (20.4 kB view details)

Uploaded Python 3

File details

Details for the file sweights-1.2.0.tar.gz.

File metadata

  • Download URL: sweights-1.2.0.tar.gz
  • Upload date:
  • Size: 619.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.1.0 CPython/3.12.4

File hashes

Hashes for sweights-1.2.0.tar.gz
Algorithm Hash digest
SHA256 576d0b0a3309728e7609e4bc72b538c9b25450cc03b4446bd591c46952bf32cf
MD5 47bf4e15830d8214e645e0c558ae81bb
BLAKE2b-256 faae6dd55e8a3a00d3977c0c916e188747bfe5f40d63383dd0a3c34537af00fe

See more details on using hashes here.

File details

Details for the file sweights-1.2.0-py3-none-any.whl.

File metadata

  • Download URL: sweights-1.2.0-py3-none-any.whl
  • Upload date:
  • Size: 20.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.1.0 CPython/3.12.4

File hashes

Hashes for sweights-1.2.0-py3-none-any.whl
Algorithm Hash digest
SHA256 32947be233959d01f43a3dc0fa9a78e5c4c3ddb42e48b07d32756105b98a1e90
MD5 8edc251e550fa3c3aa97660af05bb34d
BLAKE2b-256 c6df1319dc883fc7580b313815e3554e952cc6f197138a8262fd47df6358d2a6

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