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velocity-estimation

Velocity estimation algorithm for imaging data in turbulent flows

https://github.com/uit-cosmo/velocity-estimation

Science Score: 44.0%

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    Low similarity (7.1%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Velocity estimation algorithm for imaging data in turbulent flows

Basic Info
  • Host: GitHub
  • Owner: uit-cosmo
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 270 KB
Statistics
  • Stars: 1
  • Watchers: 3
  • Forks: 1
  • Open Issues: 2
  • Releases: 0
Created over 2 years ago · Last pushed 10 months ago
Metadata Files
Readme License Citation

README.md

velocity-estimation

Two dimensional velocity estimation methods for coarse-grained imaging data. Traditional methods compute the velocity components in a given directions from the time delay between signals separated in such direction. This approach is inaccurate and can lead to big errors if the velocity of propagation is not aligned with the separation between the two measurement points. At least three points need to be considered, and time delays in two different directions need to be used simultaneously for the accurate estimation of the velocity vector. The code in this repository implements such method for imaging data. The underlying time delay estimation can be switched from cross-correlation analysis or cross-conditional averaging.

Documentation here.

Install

git clone https://github.com/uit-cosmo/velocity-estimation.git cd velocity-estimation pip install .

Use

The main function is twodimvelocityestimates.estimatevelocity_field(ds, eo), its usage is described in a notebook under the guides/ folder.

Owner

  • Name: Complex Systems Modelling - UiT
  • Login: uit-cosmo
  • Kind: organization

Research group at UiT devoted to modelling of complex physical, biological, ecological and socio-economic systems.

Citation (CITATION.cff) 

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: velocity_estimation
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Juan Manuel
    family-names: Losada
    email: juan.m.losada@uit.no
    affiliation: UiT The Arctic University of Norway
    orcid: 'https://orcid.org/0000-0003-2054-1384'
repository-code: 'https://github.com/uit-cosmo/velocity-estimation'
abstract: >-
  Two dimensional velocity estimation methods for
  coarse-grained imaging data. Traditional methods compute
  the velocity components in a given directions from the
  time delay between signals separated in such direction.
  This approach is inaccurate and can lead to big errors if
  the velocity of propagation is not aligned with the
  separation between the two measurement points. At least
  three points need to be considered, and time delays in two
  different directions need to be used simultaneously for
  the accurate estimation of the velocity vector. The code
  in this repository implements such method for imaging
  data. The underlying time delay estimation can be switched
  from cross-correlation analysis or cross-conditional
  averaging.
license: MIT

GitHub Events

Total
  • Issues event: 2
  • Push event: 26
  • Pull request event: 7
  • Create event: 5
Last Year
  • Issues event: 2
  • Push event: 26
  • Pull request event: 7
  • Create event: 5

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 2
  • Total pull requests: 4
  • Average time to close issues: N/A
  • Average time to close pull requests: less than a minute
  • Total issue authors: 1
  • Total pull request authors: 1
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 3
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 2
  • Pull requests: 4
  • Average time to close issues: N/A
  • Average time to close pull requests: less than a minute
  • Issue authors: 1
  • Pull request authors: 1
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 3
  • Bot issues: 0
  • Bot pull requests: 0
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