fft_metrics function

Discrete Fourier Transform Metrics

This is a wrapper function to calculate all the DFFT metrics for the given input signal

fft_metrics(data, candmin, candmax, river.name = "",
  baseline.signal = NULL)

Arguments

• data: A matrix with dates in the first column and discharge values in the second column. Dates should be of the format "YYYY-MM-DD"
• candmin: numeric vector of possible ordinal days in which the predicted signal is lowest. This range need not be narrow, but a string of consecutive days should not include more than only local minimum. Used for calculating the high- and low-flow windows
• candmax: numeric vector of possible ordinal days in which the predicted signal is highest. This range need not be narrow, but a string of consecutive days should not include more than only local maximum.
• river.name: A character vector listing the river name.
• baseline.signal: If NULL, this function calculates baseline.signal using fourierAnalysis over the entire input series. The baseline signal can also be explicitly calculated and passed in as parameter. Check function prepareBaseline()

Returns

A list containing 2 data frames:

high.level.metrics	Data frame containing NAA and FPExt values for each year in the given series
naa.shape.components	Data frame containing HSAM, LSAM, Transition time, HSAF, LSAF, timing of HSAM, timing of LSAM, IFI, IDI

Examples

# load sample data
data("sycamore")
x = sycamore

# get streamflow object for the sample data
x.streamflow = asStreamflow(x)

# prepare baseline signal 
x.bl = prepareBaseline(x.streamflow)

# fetch the DFFT metrics for this sample data
# "candmax" chosen because preliminary analysis (e.g. with fourierAnalysis 
#           output) shows the signal is highest sometime between
#           day 190 and day 330
# "candmin" can be estimated analogously.
x.fftmetrics = fft_metrics(x, river.name = "Sycamore", candmin = c(40:125), 
                           candmax = c(190:330), baseline.signal = x.bl)