Skip to content
Snippets Groups Projects
Select Git revision
  • Stationary
  • cfl_number_computation
  • datfile
  • master default protected
  • python_update
  • timeseries_documentation
  • turbulence_generator
  • version_badge
  • v0.2.4
  • v0.2.3
  • v0.2.2
  • v0.2.0
  • v0.1.11
  • v0.1.10
  • v0.1.9
  • v0.1.8
  • v0.1.7
  • v0.1.6
  • v0.1.5
  • v0.1.4
  • v0.1.3
  • v0.1.2
  • v0.1.1
  • v0.1.0
  • v0.0.1
  • pre
26 results
You can move around the graph by using the arrow keys.
Created with Raphaël 2.2.031Oct3025241614121028Sep201911429Aug23Jul161587411Jun28May191220Mar1917161531Oct271413123Aug26Jul1715654327Jun2523213131May24211920Apr18136315Mar1126Feb25239Jan87543130Dec272524232221181754225Nov23222018171482131Oct292827252015Sep19Aug136549Jun876131May2925243223Apr22131128Mar2724212018106523Feb21201918176531Jan2923Dec2120181715131210hotfix nondimensionalsv0.2.2v0.2.2version incrementationhotfix nondimensionals: now compatible to more then just OpenFOAM simulationsMerge branch 'cleanup' into 'master'hotfix nondimensionalsMerge branch 'cleanup' into 'master'v0.2.0v0.2.0adapt stationarity tolerance -> reliabilityMerge branch 'cleanup' into 'master'failsafe tele fixfailsafe tele fiximprove tele fiximprove tele fixMerge remote-tracking branch 'origin/cleanup' into cleanupversion infoversion infocleanupfix jupyter after fluidfoam upgradeclean obsoletedependency upgradesupgradesfix testreformatting and cleanupadd open3d to requirements. maybe useful for postprocessing too?statistical voronoi voxelization le/te detection for error resistancyadd open3d to requirement. maybe also useful for postprocessingfixing all testspyproject.toml installationremove rolled blade datasetcleanup. remove unnecessary and complex code. remove unidentified bugsinteger fixmaybe working? performance is bad thoughoptimization improvementsintermediateoptimizer improvements - but still not reliable?adding stationarity score (lowe value r-> stationary signal) as the algorithm wasnt able to handle instationary signalsthere might be an instationary error and a stationary error. we can work with the instationary error, because we are minmax normalizing the series in the beginning. the series might start at 1 and goes to 0 or the other way round - it makes sense to allow a small instationary error which is controllable with a minmax normalized seriesintermediateinitial midline optimization commitinitial midline optimization commitsimplify optimal window size