Package: psdr 1.0.3

psdr: Use Time Series to Generate and Compare Power Spectral Density

Functions that allow you to generate and compare power spectral density (PSD) plots given time series data. Fast Fourier Transform (FFT) is used to take a time series data, analyze the oscillations, and then output the frequencies of these oscillations in the time series in the form of a PSD plot.Thus given a time series, the dominant frequencies in the time series can be identified. Additional functions in this package allow the dominant frequencies of multiple groups of time series to be compared with each other. To see example usage with the main functions of this package, please visit this site: <https://yhhc2.github.io/psdr/articles/Introduction.html>. The mathematical operations used to generate the PSDs are described in these sites: <https://www.mathworks.com/help/matlab/ref/fft.html>. <https://www.mathworks.com/help/signal/ug/power-spectral-density-estimates-using-fft.html>.

Authors:Yong-Han Hank Cheng [aut, cre]

psdr_1.0.3.tar.gz
psdr_1.0.3.zip(r-4.7-any)psdr_1.0.3.zip(r-4.6-any)psdr_1.0.3.zip(r-4.5-any)
psdr_1.0.3.tgz(r-4.6-any)psdr_1.0.3.tgz(r-4.5-any)
psdr_1.0.3.tar.gz(r-4.7-any)psdr_1.0.3.tar.gz(r-4.6-any)
psdr_1.0.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
psdr/json (API)

# Install 'psdr' in R:
install.packages('psdr', repos = c('https://yhankcheng.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.00 score 4 scripts 403 downloads 17 exports 17 dependencies

Last updated from:cc025c9152. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK111
source / vignettesOK182
linux-releaseOK123
macos-releaseOK186
macos-oldrelOK201
windows-develOK77
windows-releaseOK115
windows-oldrelOK69
wasm-releaseOK101

Exports:AutomatedCompositePlottingCountWindowsFindHomogeneousWindowsGenerateExampleDataGetHomogeneousWindowsGetSubsetOfWindowsGetSubsetOfWindowsTwoLevelsIdentifyMaxOnXYMakeCompositePSDForAllWindowsMakeCompositeXYPlotForAllWindowsMakeOneSidedAmplitudeSpectrumMakePowerSpectralDensityPSDDominantFrequencyForMultipleWindowsPSDIdentifyDominantFrequencyPSDIntegrationPerFreqBinSingleBinPSDIntegrationForMultipleWindowsSingleBinPSDIntegrationOrDominantFreqComparison

Dependencies:clicpp11farverggplot2gluegtableisobandlabelinglifecycleR6RColorBrewerrlangS7scalesvctrsviridisLitewithr

Examples
Overview | Installation | AutomatedCompositePlotting() | CountWindows() | FindHomogeneousWindows() | GetHomogeneousWindows | GetSubsetOfWindows() | GetSubsetOfWindowsTwoLevels() | IdentifyMaxOnXY() | MakeCompositePSDForAllWindows() | MakeCompositeXYPlotForAllWindows | MakeOneSidedAmplitudeSpectrum() | MakePowerSpectralDensity() | PSDDominantFrequencyForMultipleWindows() | PSDIdentifyDominantFrequency() | PSDIntegrationPerFreqBin() | SingleBinPSDIntegrationForMultipleWindows() | SingleBinPSDIntegrationOrDominantFreqComparison()
Rendered fromExamples.Rmdusingknitr::rmarkdown

Last update: 2021-06-04
Started: 2021-06-04

Introduction
Overview | Installation | Example | Load in example dataset | Explore the dataset | Visualize the frequency contribution of signals | Comparing frequency contribution of each category
Rendered fromIntroduction.Rmdusingknitr::rmarkdown

Last update: 2021-06-04
Started: 2021-06-04

Readme and manuals

Help Manual

Help pageTopics
Automated plotting of time series, PSD, and log transformed PSDAutomatedCompositePlotting
Create a contingency table to display how many windows (dataframes) fall into particular categoriesCountWindows
Assess if window (dataframe) share certain features across all observationsFindHomogeneousWindows
Produce example data set for demonstrating package functionsGenerateExampleData
Get windows (dataframes) that have the same specified column values for all observationsGetHomogeneousWindows
Select only windows (dataframes) where a specified column matches a specified valueGetSubsetOfWindows
Select only windows (dataframes) where a two specified columns must match specified valuesGetSubsetOfWindowsTwoLevels
Given a xy plot. Find the maximum value on the plotIdentifyMaxOnXY
Make PSD for each window (dataframe) in a list and then find the average of all the PSDsMakeCompositePSDForAllWindows
Find averaged xy plotsMakeCompositeXYPlotForAllWindows
Create a one sided amplitude spectrum using time series dataMakeOneSidedAmplitudeSpectrum
Create a power spectral density (PSD) plot using time series dataMakePowerSpectralDensity
Calculate dominant frequency for multiple PSDs for a single frequency rangePSDDominantFrequencyForMultipleWindows
Given a time series vector, create a PSD and find the dominant frequencyPSDIdentifyDominantFrequency
Given a time series vector, generate a PSD, then calculate integration for specified binsPSDIntegrationPerFreqBin
Calculate integral for multiple PSDs for a single frequency binSingleBinPSDIntegrationForMultipleWindows
Given sets of windows (dataframes) corresponding to different combos, see if the integration or dominant frequency of a specific frequency range is significantly different between the combosSingleBinPSDIntegrationOrDominantFreqComparison