Skip to main content

This is the code behind the paper 'Translating jet noise measurement to near-field level maps with nearest neighbor bilinear smoothing interpolation'

Project description

Nearest Neighbor Bi-linear Acoustic Interpolation

This code based was used for the development of near-field acoustic level maps associated with the paper: Mobley, Frank S., Alan T. Wall, and Stephen C. Campbell. "Translating jet noise measurements to near-field level maps with nearest neighbor bilinear smoothing interpolation." The Journal of the Acoustical Society of America 150.2 (2021): 687-693.

This code applies an image interpolation to a sparse matrix of acoustic levels. Then interpolates the dense matrix interatively to reduce the blockiness of the level maps. Each iteration, the known values from the sparse matrix are replaced to ensure that the values are not smoothed away.

This work was completed in association with research conducted at Wright-Patteron Air Force Base while employed at the 711 Human Performance Wing. It was cleared for public release with the public affairs clearance number AFRL-2025-2605 on 21 May 2025.

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

Built Distribution

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

File details

Details for the file nearest_neighbor_acoustic_interpolation-0.1.2.tar.gz.

File metadata

File hashes

Hashes for nearest_neighbor_acoustic_interpolation-0.1.2.tar.gz
Algorithm Hash digest
SHA256 a623e7369133ca6d81452026c9cc244332015ef62476453e85c63bafda773d16
MD5 9530e2160c55201e25fa59fc03f1bf8b
BLAKE2b-256 bcb291a344e78f1a536e8046bf22792a7688a92d0f251524728e4fc795b3b1b9

See more details on using hashes here.

File details

Details for the file nearest_neighbor_acoustic_interpolation-0.1.2-py3-none-any.whl.

File metadata

File hashes

Hashes for nearest_neighbor_acoustic_interpolation-0.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 e73359c358ae408b99cccb202eb7c991333a95f75b784176b976444bb38afb44
MD5 16e32ab747963247fc4bba68453363d6
BLAKE2b-256 2e7c4e5cf8db1312209b0236caebae4ce3d4990686aefac4337045184a8c6a74

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