introduce develop method + changed data

NAMESPACE

 # Generated by roxygen2: do not edit by hand
 
-S3method(as.data.frame,mfft_deco)
-S3method(lines,mfft_deco)
+S3method(as.data.frame,discreteSpectrum)
+S3method(develop,discreteSpectrum)
+S3method(lines,discreteSpectrum)
 S3method(plot,SSAObject)
+S3method(plot,discreteSpectrum)
 S3method(plot,memObject)
-S3method(plot,mfft_deco)
 S3method(plot,periodogram)
 S3method(plot,wavelet)
-S3method(print,mfft_deco)
+S3method(print,discreteSpectrum)
 export(approx_ts)
 export(arspec)
 export(attributeTones)
 export(cwt_morlet)
+export(develop)
 export(hilbert_extension)
 export(mem)
 export(mfft)
@@ -21,7 +23,6 @@ export(mfft_real)
 export(mmfft)
 export(periodogram)
 export(powerspectrum.wavelet)
-export(reconstruct_mfft)
 export(reconstruct_morlet)
 export(ssa)
 export(toneCombinations)

R/data_description.R

@@ -2,7 +2,7 @@
 #'
 #' A simple time series for testing purposes
 #' cos(t*0.13423167+0.00) + 1.3 * cos(t*0.119432+2.314) + 0.134994 + 0.4*cos(t*0.653167) + 0.11 * cos(t*0.78913498) 
-#' for t from 0 to 1023. It is coded as a `mfft_deco` objcet and self-describes its spectral decomposition. 
+#' for t from 0 to 1023. It is coded as a `discreteSpectrum` objcet and self-describes its spectral decomposition. 
 #'
 #' @docType data
 #' @keywords datasets
@@ -14,7 +14,7 @@ NULL
 #'
 #' Same has harmonic sample but with an added random time series
 #' cos(t*0.13423167+0.00) + 1.3 * cos(t*0.119432+2.314) + 0.134994 + 0.4*cos(t*0.653167) + 0.11 * cos(t*0.78913498) + rnorm(1024)*0.12
-#' for t from 0 to 1023. It is coded as a `mfft_deco` objcet and self-describes its spectral decomposition. 
+#' for t from 0 to 1023. It is coded as a `discreteSpectrum` objcet and self-describes its spectral decomposition. 
 #'
 #' @docType data
 #' @keywords datasets
@@ -26,7 +26,7 @@ NULL
 #'
 #' A truncated version (squared-shape) version of a harmonic signal for mimicking a sedimentary log
 #' sign( cos(t*0.13423167+0.00) + 1.3 * cos(t*0.119432+2.314) + 0.134994 + 0.4*cos(t*0.653167) + 0.11 * cos(t*0.78913498) + 0.8))
-#' for t from 0 to 8191. It is coded as a `mfft_deco` objcet and self-describes its spectral decomposition. 
+#' for t from 0 to 8191. It is coded as a `discreteSpectrum` objcet and self-describes its spectral decomposition. 
 #' @docType data
 #' @keywords datasets
 #' @name pseudo_log

R/mfft_complex.R

@@ -22,7 +22,7 @@
 #' not documented for real time series
 #' @param nfreq is the number of frequencies returned, must be smaller that the length of  xdata.
 #' @param force_complex : use the complex number implementation even if the time series is real. 
-#' @return a `mfft_deco` object, based on a data.frame with columns "Freq", "Ampl" and "Phases". 
+#' @return a `discreteSpectrum` object, based on a data.frame with columns "Freq", "Ampl" and "Phases". 
 #'         note that because of a language glitch (to be fixed), "Freq" actually means "Rate"
 #' @author Michel Crucifix for the R code, and David Nesvorny for most of the supporting C code doing the
 #'         Frequency Modified Fourier transform  for Complex Numbers
@@ -56,7 +56,7 @@ mfft <- function(xdata, nfreq=15, minfreq=NULL, maxfreq=NULL, correction=1, forc
 #' the third algorithm should be in general much more precise).  
 #' The computed frequencies are in the range given by minfreq and maxfreq.
 #' @param nfreq is the number of frequencies returned, must be smaller that the length of  xdata.
-#' @return a `mfft_deco` object, based on a data.frame with columns "Freq", "Ampl" and "Phases". 
+#' @return a `discreteSpectrum` object, based on a data.frame with columns "Freq", "Ampl" and "Phases". 
 #'         note that because of a language glitch (to be fixed), "Freq" actually means "Rate"
 #' @author Michel Crucifix for the R code, and David Nesvorny for most of the supporting C code doing the
 #' actual computations
@@ -104,7 +104,7 @@ mfft_complex <- function(xdata, nfreq=30,  minfreq=NULL, maxfreq=NULL, correctio
      # corresponding to the same amplitude
      OUT <- data.frame(Freq=Freq, Amp=Ampl, Phases=Phase)
 
-     class(OUT) <- c("mfft_deco", "data.frame")
+     class(OUT) <- c("discreteSpectrum", "data.frame")
      attr(OUT,"nfreq") <- nfreq
      attr(OUT,"data") <- xdata
 

R/mfft_real.R

@@ -342,15 +342,15 @@ mfft_analyse <- function(xdata, nfreq, fast = TRUE, nu = NULL, minfreq=NULL, max
 #' @param nfreq is the number of frequencies returned, must be smaller that the length of  xdata.
 #' @param fast (default = TRUE) uses analytical formulations for the crossproducts involving sines and cosines. 
 #'        note: this is not really faster because the bottleneck is actually the goden section search. But more elegant. 
-#' @return a `mfft_deco` object, based on a data.frame with columns "Freq", "Ampl" and "Phases". 
+#' @return a `discreteSpectrum` object, based on a data.frame with columns "Freq", "Ampl" and "Phases". 
 #' @author Michel Crucifix
 #' @references
 #' \insertRef{sidlichovsky97aa}{gtseries}
 #' @examples
 #' 
 #' data(harmonic_sample)
-#' spectrum <- mfft_real(harmonic_sample$data)
-#' print(spectrum)
+#' spec <- mfft_real(harmonic_sample$data)
+#' print(spec)
 #'
 #' @export mfft_real
 mfft_real <- function(xdata, nfreq=5,  minfreq=NULL, maxfreq=NULL, correction = 1 , fast=TRUE){
@@ -430,7 +430,7 @@ mfft_real <- function(xdata, nfreq=5,  minfreq=NULL, maxfreq=NULL, correction =
 
     # rename for class compatibility
     names(OUT) <- c("Freq","Amp","Phases")
-    class(OUT) <- c("mfft_deco", "data.frame")
+    class(OUT) <- c("discreteSpectrum", "data.frame")
     attr(OUT, "data")  <- xdata
     attr(OUT, "nfreq")  <- nfreq
     return(OUT)

R/mfft_support.R

 #' MFFT reconstruction
 #' @importFrom stats deltat start
-#' @rdname mfft_deco
-#' @param  M : mfft_deco object 
-#' @param  sum : TRUE if user wants to sum components in the reconstruction
-#' @export reconstruct_mfft
+#' @param  M : discreteSpectrum object 
+#' @param  times: if supplied, times of the decomposition
+#' @param  start: if supplied, overrides time and will generate a time series with start and deltat, which must then
+#'         be supplied as well
+#' @param  deltat : see start. 
+#' @param  sum : TRUE if user wants to sum components %in% the reconstruction
+#' @note   if none if times, start and deltat are supplied, will reconstruct based on the attribute `xdata`
+#'         which must then be present. If no `xdata` is availble, return an error. 
 #' @return list of reconstructed components if sum=FALSE,  full
 #'         reconstructed time series otherwise
-reconstruct_mfft  <- function(M, sum=TRUE){
- if (!(attr(M,"class")=="mfft_deco")) stop ("object is not a MFFT decomposition")
+#' @method develop discreteSpectrum
+#' @export
+develop.discreteSpectrum  <- function(M, times=NULL, start=NULL, end=NULL, deltat=NULL, sum=TRUE){
+ if (!("discreteSpectrum" %in% class(M))) stop ("object is not a discreteSpectrum decomposition")
+
+ timesIsATseries = FALSE
+ if (!is.null(start)){
+   if (is.null(deltat) || is.null(end)) stop ("if you supply start, you must also supply deltat and end");
+   times <- start + seq((end - start) %/% deltat) * deltat
+   timesIsATseries = TRUE
+ }
+
+ if (is.null(times)){
+   if (is.null(attr(M,"data"))) stop ("if you do not supply any time argument (times, or (start, end, deltat)), then object must have a valid data attribute")
  xdata <- attr(M,"data")
+ start <- stats::start(xdata)
+ deltat <- stats::deltat(deltat)
+ times <- (seq(length(xdata))-1) * stats::deltat(xdata) + stats::start(xdata)[1]
+ timesIsATseries = TRUE
+ }
+
  nfreq <- attr(M,"nfreq")
- times <- seq(length(xdata))*stats::deltat(xdata) + stats::start(xdata)[1]
- reconstructed <- lapply(seq(nfreq), function(i) ts( M$Amp[i]*cos(M$Freq[i]*times + M$Phase[i]), start=stats::start(xdata), deltat=stats::deltat(xdata)) )
+ if (is.null(nfreq)) nfreq <- length(M$Amp)
+ if (timesIsATseries){
+   reconstructed <- lapply(seq(nfreq), function(i) ts( M$Amp[i] * cos(M$Freq[i] * times + M$Phase[i]), start=start, deltat=deltat) )} 
+ else {
+   reconstructed <- lapply(seq(nfreq), function(i) M$Amp[i] * cos(M$Freq[i] * times + M$Phase[i]))
+ }
 
  if ( sum ) reconstructed <- ts(apply(simplify2array(reconstructed), 1 , sum), start=stats::start(xdata), deltat=stats::deltat(xdata))
+ return(reconstructed)
 }
 
 #' MFFT ANOVA
 #' not ready. do not use. 
-#' @rdname mfft_deco
+#' @rdname discreteSpectrum
 #' @export mfft_anova
 mfft_anova  <- function(M){
- if (!(attr(M,"class")=="mfft_deco")) stop ("object is not a MFFT decomposition")
+ if (!("discreteSpectrum" %in% class(M))) stop ("object is not a discreteSpectrum decomposition")
  xdata <- attr(M,"xdata")
  nfreq <- attr(M,"nfreq")
  N <- length(xdata)
@@ -37,17 +64,17 @@ mfft_anova  <- function(M){
 }
 
 
-#' @rdname mfft_deco
+#' @rdname discreteSpectrum
 #' @export
-as.data.frame.mfft_deco <- function(x) {data.frame(Freq=x$Freq, Amp=x$Amp, Phases=x$Phases)}
+as.data.frame.discreteSpectrum <- function(x) {data.frame(Freq=x$Freq, Amp=x$Amp, Phases=x$Phases)}
 
 
-#' @rdname mfft_deco
-#' @param a `mfft_deco` object, typically the output of a `mfft` call. 
+#' @rdname discreteSpectrum
+#' @param a `discreteSpectrum` object, typically the output of a `mfft` call. 
 #' @param labels to be set above the frequency peaks. Can be the output of `attributeTone`
 #' @param periods if TRUE will add a lower axis with period labels
 #' @export
-plot.mfft_deco <- function (M,periods=FALSE,labels=NULL,...){
+plot.discreteSpectrum <- function (M,periods=FALSE,labels=NULL,...){
 #   O <- order(M$Freq)
   plot(abs(M$Freq), abs(M$Amp),'h',ylab="Amplitudes", xlab="",  ...)
   if (periods) {
@@ -60,25 +87,25 @@ plot.mfft_deco <- function (M,periods=FALSE,labels=NULL,...){
   } else {
     mtext("Rate", 1, 3)
   }
-  points(abs(M$Freq), abs(M$Amp),'p',...)
+  # points(abs(M$Freq), abs(M$Amp),'p',...)
   if (!is.null(labels)) {
     yshift <- 0.05*diff(range(M$Amp))
     text(M$Freq, M$Amp, labels, srt=90, , adj=-0.4)
   }
 }
 
-#' @rdname mfft_deco
+#' @rdname discreteSpectrum
 #' @export
-lines.mfft_deco <- function (M,...){
+lines.discreteSpectrum <- function (M,...){
 #   O <- order(M$Freq)
   lines(abs(M$Freq), abs(M$Amp),'h',...)
   points(abs(M$Freq), abs(M$Amp),'p',...)
 }
 
 
-#' @rdname mfft_deco
+#' @rdname discreteSpectrum
 #' @export
-print.mfft_deco <- function (M,...){
+print.discreteSpectrum <- function (M,...){
   print.data.frame(cbind(as.data.frame(M), Period=2*pi/M$Freq))
 }
 

data-raw/harmonic_sample.R

@@ -10,12 +10,10 @@ harmonic_sample_spectrum <- list(
   Freq = c(0.13423167, 0.119432, 0, 0.653167, 0.78913498),
   Phases = c(0, 2.314, 0, 0.653167, 0))
 
-attr(harmonic_sample_spectrum,"class") <- 'mfft_deco'
-
-harmonic_sample_noisy$data <- harmonic_sample_data
-harmonic_sample_noisy$spectrum <- harmonic_sample_spectrum
-
+class(harmonic_sample_spectrum) <- 'discreteSpectrum'
 
+harmonic_sample_noisy <- harmonic_sample_spectrum
+attr(harmonic_sample_noisy,"data") <- harmonic_sample_data
 
 usethis::use_data(harmonic_sample_noisy, overwrite = TRUE)
 
@@ -29,10 +27,9 @@ harmonic_sample_spectrum <- list(
   Freq = c(0.13423167, 0.119432, 0, 0.653167, 0.78913498),
   Phases = c(0, 2.314, 0, 0.653167, 0))
 
-attr(harmonic_sample_spectrum,"class") <- 'mfft_deco'
-
-harmonic_sample$data <- harmonic_sample_data
-harmonic_sample$spectrum <- harmonic_sample_spectrum
+class(harmonic_sample_spectrum) <- 'discreteSpectrum'
 
+harmonic_sample <- harmonic_sample_spectrum
+attr(harmonic_sample,"data") <- harmonic_sample_data
 
 usethis::use_data(harmonic_sample, overwrite = TRUE)

data-raw/pseudo_log.R

@@ -13,10 +13,10 @@ pseudo_log_spectrum <- list(
   Phases = c(0, 2.314, 0, 0.653167, 0))
 
 
-attr(pseudo_log_spectrum,"class") <- 'mfft_deco'
+class(pseudo_log_spectrum) <- 'discreteSpectrum'
 
-pseudo_log$data <- pseudo_log_data
-pseudo_log$spectrum <- pseudo_log_spectrum
+pseudo_log_spectrum <- pseudo_log_spectrum
+attr(pseudo_log,"data")  <- pseudo_log_data
 
 
 usethis::use_data(pseudo_log, overwrite = TRUE)

man/harmonic_sample.Rd

@@ -10,6 +10,6 @@ data(harmonic_sample)
 \description{
 A simple time series for testing purposes
 cos(t*0.13423167+0.00) + 1.3 * cos(t*0.119432+2.314) + 0.134994 + 0.4*cos(t*0.653167) + 0.11 * cos(t*0.78913498) 
-for t from 0 to 1023. It is coded as a `mfft_deco` objcet and self-describes its spectral decomposition.
+for t from 0 to 1023. It is coded as a `discreteSpectrum` objcet and self-describes its spectral decomposition.
 }
 \keyword{datasets}

man/harmonic_sample_noisy.Rd

@@ -10,6 +10,6 @@ data(harmonic_sample_noisy)
 \description{
 Same has harmonic sample but with an added random time series
 cos(t*0.13423167+0.00) + 1.3 * cos(t*0.119432+2.314) + 0.134994 + 0.4*cos(t*0.653167) + 0.11 * cos(t*0.78913498) + rnorm(1024)*0.12
-for t from 0 to 1023. It is coded as a `mfft_deco` objcet and self-describes its spectral decomposition.
+for t from 0 to 1023. It is coded as a `discreteSpectrum` objcet and self-describes its spectral decomposition.
 }
 \keyword{datasets}

man/mfft.Rd

@@ -31,7 +31,7 @@ The computed frequencies are in the range given by minfreq and maxfreq.}
 not documented for real time series}
 }
 \value{
-a `mfft_deco` object, based on a data.frame with columns "Freq", "Ampl" and "Phases". 
+a `discreteSpectrum` object, based on a data.frame with columns "Freq", "Ampl" and "Phases". 
         note that because of a language glitch (to be fixed), "Freq" actually means "Rate"
 }
 \description{

man/mfft_complex.Rd

@@ -25,7 +25,7 @@ the third algorithm should be in general much more precise).
 The computed frequencies are in the range given by minfreq and maxfreq.}
 }
 \value{
-a `mfft_deco` object, based on a data.frame with columns "Freq", "Ampl" and "Phases". 
+a `discreteSpectrum` object, based on a data.frame with columns "Freq", "Ampl" and "Phases". 
         note that because of a language glitch (to be fixed), "Freq" actually means "Rate"
 }
 \description{

man/mfft_deco.Rd

-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/mfft_support.R
-\name{reconstruct_mfft}
-\alias{reconstruct_mfft}
-\alias{mfft_anova}
-\alias{as.data.frame.mfft_deco}
-\alias{plot.mfft_deco}
-\alias{lines.mfft_deco}
-\alias{print.mfft_deco}
-\title{MFFT reconstruction}
-\usage{
-reconstruct_mfft(M, sum = TRUE)
-
-mfft_anova(M)
-
-\method{as.data.frame}{mfft_deco}(x)
-
-\method{plot}{mfft_deco}(M, periods = FALSE, labels = NULL, ...)
-
-\method{lines}{mfft_deco}(M, ...)
-
-\method{print}{mfft_deco}(M, ...)
-}
-\arguments{
-\item{M}{: mfft_deco object}
-
-\item{sum}{: TRUE if user wants to sum components in the reconstruction}
-
-\item{periods}{if TRUE will add a lower axis with period labels}
-
-\item{labels}{to be set above the frequency peaks. Can be the output of `attributeTone`}
-
-\item{a}{`mfft_deco` object, typically the output of a `mfft` call.}
-}
-\value{
-list of reconstructed components if sum=FALSE,  full
-        reconstructed time series otherwise
-}
-\description{
-MFFT reconstruction
-
-MFFT ANOVA
-not ready. do not use.
-}

man/mfft_real.Rd

@@ -29,7 +29,7 @@ note: this is not really faster because the bottleneck is actually the goden sec
 3: second order-correction using synthetic data (all documented in the Sidlichovsky and Nesvorny reference)}
 }
 \value{
-a `mfft_deco` object, based on a data.frame with columns "Freq", "Ampl" and "Phases".
+a `discreteSpectrum` object, based on a data.frame with columns "Freq", "Ampl" and "Phases".
 }
 \description{
 R-coded version of the Modified Fourier Transform
@@ -43,8 +43,8 @@ A C-version should be supplied one day.
 \examples{
 
 data(harmonic_sample)
-spectrum <- mfft_real(harmonic_sample$data)
-print(spectrum)
+spec <- mfft_real(harmonic_sample$data)
+print(spec)
 
 }
 \references{

man/pseudo_log.Rd

@@ -10,6 +10,6 @@ data(pseudo_log)
 \description{
 A truncated version (squared-shape) version of a harmonic signal for mimicking a sedimentary log
 sign( cos(t*0.13423167+0.00) + 1.3 * cos(t*0.119432+2.314) + 0.134994 + 0.4*cos(t*0.653167) + 0.11 * cos(t*0.78913498) + 0.8))
-for t from 0 to 8191. It is coded as a `mfft_deco` objcet and self-describes its spectral decomposition.
+for t from 0 to 8191. It is coded as a `discreteSpectrum` objcet and self-describes its spectral decomposition.
 }
 \keyword{datasets}

notebook/mfft_random_test.ipynb

@@ -259,7 +259,7 @@
        "[1] 0.000000 2.314000 0.000000 0.653167 0.000000\n",
        "\n",
        "attr(,\"class\")\n",
-       "[1] \"mfft_deco\"\n"
+       "[1] \"spectrum\"\n"
       ]
      },
      "metadata": {},
@@ -3832,7 +3832,7 @@
        "[1] -5.4832704  0.4030191 -1.6431082  2.3957822 -1.1782940  0.0000000\n",
        "\n",
        "attr(,\"class\")\n",
-       "[1] \"mfft_deco\"\n",
+       "[1] \"spectrum\"\n",
        "attr(,\"row.names\")\n",
        "[1] 1 2 3 4 5 6\n",
        "attr(,\"nfreq\")\n",
@@ -4269,7 +4269,7 @@
        "[1] -0.6804843 -1.4340620 -2.6011862  0.2136524  0.0000000 -5.6976628  0.5740075\n",
        "\n",
        "attr(,\"class\")\n",
-       "[1] \"mfft_deco\"\n",
+       "[1] \"spectrum\"\n",
        "attr(,\"row.names\")\n",
        "[1] 1 2 3 4 5 6 7\n",
        "attr(,\"nfreq\")\n",
@@ -4481,7 +4481,7 @@
        "[1]  0.0000000 -1.6515515 -1.5786453 -1.4144484  2.8983504 -0.8902084\n",
        "\n",
        "attr(,\"class\")\n",
-       "[1] \"mfft_deco\"\n",
+       "[1] \"spectrum\"\n",
        "attr(,\"row.names\")\n",
        "[1] 1 2 3 4 5 6\n",
        "attr(,\"nfreq\")\n",