| Type: | Package |
| Title: | Continuous Wavelet Transformation for Spectroscopy |
| Version: | 0.2.1 |
| Maintainer: | J. Antonio Guzmán Q. <antguz06@gmail.com> |
| Description: | Fast application of Continuous Wavelet Transformation ('CWT') on time series with special attention to spectroscopy. It is written using data.table and 'C++' language and in some functions it is possible to use parallel processing to speed-up the computation over samples. Currently, only the second derivative of a Gaussian wavelet function is implemented. |
| License: | GPL (≥ 3) |
| URL: | https://github.com/Antguz/CWT |
| BugReports: | https://github.com/Antguz/CWT/issues |
| Depends: | R (≥ 4.0.0) |
| Imports: | data.table (≥ 1.14.0), Rcpp |
| Suggests: | testthat (≥ 3.2.0) |
| LinkingTo: | Rcpp, RcppArmadillo |
| ByteCompile: | true |
| Config/testthat/edition: | 3 |
| Encoding: | UTF-8 |
| Language: | en-US |
| RoxygenNote: | 7.3.1 |
| SystemRequirements: | GNU make |
| NeedsCompilation: | yes |
| Packaged: | 2024-06-28 01:17:03 UTC; antonio |
| Author: | J. Antonio Guzmán Q. [cre, aut, cph] |
| Repository: | CRAN |
| Date/Publication: | 2024-06-28 03:50:02 UTC |
Continuous Wavelet Transformation for Spectroscopy
Description
Fast application of Continuous Wavelet Transformation on timeseries with special attention to spectroscopy. It is written using'data.table' and 'C++' language and in some functions it is possible touse parallel processing to speed-up the computation over samples.
Author(s)
Maintainer: J. Antonio Guzmán Q.antguz06@gmail.com (ORCID) [copyright holder]
See Also
Useful links:
Continuous Wavelet Transform
Description
Compute a 1D continuous wavelet transformation using 2st orderderivative Gaussian wavelet.
Usage
cwt(t, scales, variance = 1, summed_wavelet = FALSE, threads = 1L)Arguments
t | A |
scales | A positive |
variance | A positive |
summed_wavelet | If |
threads | An |
Value
Ifsummed_wavelet = TRUE, it returns adata.table wherecolumns are the sum of wavelet scales. Ifsummed_wavelet = FALSE, itreturns anarray (i.e., time, samples, and scales).
Author(s)
J. Antonio Guzmán Q.
Examples
time_series <- sin(seq(0, 20 * pi, length.out = 100))# Using a numeric vectorcwt(t = time_series, scales = c(1, 2, 3, 4, 5), summed_wavelet = FALSE)cwt(t = time_series, scales = c(1, 2, 3, 4, 5), summed_wavelet = TRUE)# Using a matrixtimes <- 100frame <- matrix(rep(time_series, times), nrow = times, byrow = TRUE)cwt(t = frame, scales = c(1, 2, 3, 4, 5), summed_wavelet = FALSE)cwt(t = frame, scales = c(1, 2, 3, 4, 5), summed_wavelet = TRUE)Full Width Half Maximum Resampling
Description
It resample spectra data using Full Width Half Maximum (FWHM).
Usage
resampling_FWHM(spectra, wavelengths, new_wavelengths, FWHM, threads = 1L)Arguments
spectra | A |
wavelengths | A |
new_wavelengths | A |
FWHM | A |
threads | An |
Value
It returns adata.table with the resampled spectra, where columnsare the new bands and rows are samples.
Author(s)
J. Antonio Guzmán Q.
Examples
mean <- 50sd1 <- 5sd2 <- 10n <- 100test <- matrix(c(dnorm(1:n, mean = mean, sd = sd1), dnorm(1:n, mean = mean, sd = sd2)), nrow = 2, byrow = TRUE)current_bands <- 1:nnew_bands <- seq(10, 90, by = 5)FHWM <- rep(5, length(new_bands))resampling_FWHM(spectra = test, wavelengths = current_bands, new_wavelengths = new_bands, FWHM = FHWM)