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mike-lawrence/CEEMD_example.R

Created December 29, 2011 22:51
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CEEMD demo
Raw
CEEMD_example.R
#load doMC for parallelism
library(doMC)
options(cores=4) #adjust to match your system
registerDoMC()
#load plyr to use doMC implicitly
library(plyr)
#load the toy data set
source('data.R')
str(data)
#show a bit of the data
plot(data,type='l')
#set some parameters
num_iterations = 1e2 #more iterations = more accurate IMFs but longer compute time
max_imfs = 1e1 #this value should increase with larger amounts of data
#pre-compute some values to be used below
signal_length = nrow(data)
noise_sd = sd(data$value)*.1
#set the random seed for replicability
set.seed(1)
#start a timer
start = proc.time()[3]
#run the CEEMD (this is the section to be converted to CUDA)
source('EMD.R') #the code in EMD.R will need to be converted to CUDA
from_ceemd = llply(
.parallel = TRUE
, .progress = 'text' #the progress bar isn't accurate when .parallel=TRUE; it'll be done at 100/ncores %
, .data = 1:num_iterations
, .fun = function(iteration){
noise = rnorm(signal_length,0,noise_sd)
imfs1 = emd(
xt = data$value+noise
, tt = data$time
, boundary = 'symmetric'
, max.imf = max_imfs
, plot.imf = F
, print_progress = F
)$imf
imfs2 = emd(
xt = data$value-noise
, tt = data$time
, boundary = 'symmetric'
, max.imf = max_imfs
, plot.imf = F
, print_progress = F
)$imf
min_cols = min(c(ncol(imfs1),ncol(imfs2)))
return((imfs1[,1:min_cols]+imfs2[,1:min_cols])/2) #return the average of the two IMF matricies
}
)
#print the time taken
proc.time()[3]-start
#collapse the results to a mean
imfs = matrix(0,nrow = signal_length, ncol = max_imfs)
for(i in 1:length(from_ceemd)){
imfs[,1:ncol(from_ceemd[[i]])] = (imfs[,1:ncol(from_ceemd[[i]])]+from_ceemd[[i]])/(i*2-1)
}
#drop any all-zero IMFs
imfs = imfs[,colSums(imfs)!=0]
#show a "filtered" version of the data by summing all but the few fastest-varying and slowest-varying IMFs (like a bandpass)
plot(x=data$time,y=rowSums(imfs[,3:(ncol(imfs)-2)]),type='l')
Raw
data.R
data = structure(list(time = c(0, 0.0039062, 0.0078125, 0.0117188, 0.015625,
0.0195312, 0.0234375, 0.0273438, 0.03125, 0.0351562, 0.0390625,
0.0429688, 0.046875, 0.0507812, 0.0546875, 0.0585938, 0.0625,
0.0664062, 0.0703125, 0.0742188, 0.078125, 0.0820312, 0.0859375,
0.0898438, 0.09375, 0.0976562, 0.1015625, 0.1054688, 0.109375,
0.1132812, 0.1171875, 0.1210938, 0.125, 0.1289062, 0.1328125,
0.1367188, 0.140625, 0.1445312, 0.1484375, 0.1523438, 0.15625,
0.1601562, 0.1640625, 0.1679688, 0.171875, 0.1757812, 0.1796875,
0.1835938, 0.1875, 0.1914062, 0.1953125, 0.1992188, 0.203125,
0.2070312, 0.2109375, 0.2148438, 0.21875, 0.2226562, 0.2265625,
0.2304688, 0.234375, 0.2382812, 0.2421875, 0.2460938, 0.25, 0.2539062,
0.2578125, 0.2617188, 0.265625, 0.2695312, 0.2734375, 0.2773438,
0.28125, 0.2851562, 0.2890625, 0.2929688, 0.296875, 0.3007812,
0.3046875, 0.3085938, 0.3125, 0.3164062, 0.3203125, 0.3242188,
0.328125, 0.3320312, 0.3359375, 0.3398438, 0.34375, 0.3476562,
0.3515625, 0.3554688, 0.359375, 0.3632812, 0.3671875, 0.3710938,
0.375, 0.3789062, 0.3828125, 0.3867188, 0.390625, 0.3945312,
0.3984375, 0.4023438, 0.40625, 0.4101562, 0.4140625, 0.4179688,
0.421875, 0.4257812, 0.4296875, 0.4335938, 0.4375, 0.4414062,
0.4453125, 0.4492188, 0.453125, 0.4570312, 0.4609375, 0.4648438,
0.46875, 0.4726562, 0.4765625, 0.4804688, 0.484375, 0.4882812,
0.4921875, 0.4960938, 0.5, 0.5039062, 0.5078125, 0.5117188, 0.515625,
0.5195312, 0.5234375, 0.5273438, 0.53125, 0.5351562, 0.5390625,
0.5429688, 0.546875, 0.5507812, 0.5546875, 0.5585938, 0.5625,
0.5664062, 0.5703125, 0.5742188, 0.578125, 0.5820312, 0.5859375,
0.5898438, 0.59375, 0.5976562, 0.6015625, 0.6054688, 0.609375,
0.6132812, 0.6171875, 0.6210938, 0.625, 0.6289062, 0.6328125,
0.6367188, 0.640625, 0.6445312, 0.6484375, 0.6523438, 0.65625,
0.6601562, 0.6640625, 0.6679688, 0.671875, 0.6757812, 0.6796875,
0.6835938, 0.6875, 0.6914062, 0.6953125, 0.6992188, 0.703125,
0.7070312, 0.7109375, 0.7148438, 0.71875, 0.7226562, 0.7265625,
0.7304688, 0.734375, 0.7382812, 0.7421875, 0.7460938, 0.75, 0.7539062,
0.7578125, 0.7617188, 0.765625, 0.7695312, 0.7734375, 0.7773438,
0.78125, 0.7851562, 0.7890625, 0.7929688, 0.796875, 0.8007812,
0.8046875, 0.8085938, 0.8125, 0.8164062, 0.8203125, 0.8242188,
0.828125, 0.8320312, 0.8359375, 0.8398438, 0.84375, 0.8476562,
0.8515625, 0.8554688, 0.859375, 0.8632812, 0.8671875, 0.8710938,
0.875, 0.8789062, 0.8828125, 0.8867188, 0.890625, 0.8945312,
0.8984375, 0.9023438, 0.90625, 0.9101562, 0.9140625, 0.9179688,
0.921875, 0.9257812, 0.9296875, 0.9335938, 0.9375, 0.9414062,
0.9453125, 0.9492188, 0.953125, 0.9570312, 0.9609375, 0.9648438,
0.96875, 0.9726562, 0.9765625, 0.9804688, 0.984375, 0.9882812,
0.9921875, 0.9960938, 1, 1.0039062, 1.0078125, 1.0117188, 1.015625,
1.0195312, 1.0234375, 1.0273438, 1.03125, 1.0351562, 1.0390625,
1.0429688, 1.046875, 1.0507812, 1.0546875, 1.0585938, 1.0625,
1.0664062, 1.0703125, 1.0742188, 1.078125, 1.0820312, 1.0859375,
1.0898438, 1.09375, 1.0976562, 1.1015625, 1.1054688, 1.109375,
1.1132812, 1.1171875, 1.1210938, 1.125, 1.1289062, 1.1328125,
1.1367188, 1.140625, 1.1445312, 1.1484375, 1.1523438, 1.15625,
1.1601562, 1.1640625, 1.1679688, 1.171875, 1.1757812, 1.1796875,
1.1835938, 1.1875, 1.1914062, 1.1953125, 1.1992188, 1.203125,
1.2070312, 1.2109375, 1.2148438, 1.21875, 1.2226562, 1.2265625,
1.2304688, 1.234375, 1.2382812, 1.2421875, 1.2460938, 1.25, 1.2539062,
1.2578125, 1.2617188, 1.265625, 1.2695312, 1.2734375, 1.2773438,
1.28125, 1.2851562, 1.2890625, 1.2929688, 1.296875, 1.3007812,
1.3046875, 1.3085938, 1.3125, 1.3164062, 1.3203125, 1.3242188,
1.328125, 1.3320312, 1.3359375, 1.3398438, 1.34375, 1.3476562,
1.3515625, 1.3554688, 1.359375, 1.3632812, 1.3671875, 1.3710938,
1.375, 1.3789062, 1.3828125, 1.3867188, 1.390625, 1.3945312,
1.3984375, 1.4023438, 1.40625, 1.4101562, 1.4140625, 1.4179688,
1.421875, 1.4257812, 1.4296875, 1.4335938, 1.4375, 1.4414062,
1.4453125, 1.4492188, 1.453125, 1.4570312, 1.4609375, 1.4648438,
1.46875, 1.4726562, 1.4765625, 1.4804688, 1.484375, 1.4882812,
1.4921875, 1.4960938, 1.5, 1.5039062, 1.5078125, 1.5117188, 1.515625,
1.5195312, 1.5234375, 1.5273438, 1.53125, 1.5351562, 1.5390625,
1.5429688, 1.546875, 1.5507812, 1.5546875, 1.5585938, 1.5625,
1.5664062, 1.5703125, 1.5742188, 1.578125, 1.5820312, 1.5859375,
1.5898438, 1.59375, 1.5976562, 1.6015625, 1.6054688, 1.609375,
1.6132812, 1.6171875, 1.6210938, 1.625, 1.6289062, 1.6328125,
1.6367188, 1.640625, 1.6445312, 1.6484375, 1.6523438, 1.65625,
1.6601562, 1.6640625, 1.6679688, 1.671875, 1.6757812, 1.6796875,
1.6835938, 1.6875, 1.6914062, 1.6953125, 1.6992188, 1.703125,
1.7070312, 1.7109375, 1.7148438, 1.71875, 1.7226562, 1.7265625,
1.7304688, 1.734375, 1.7382812, 1.7421875, 1.7460938, 1.75, 1.7539062,
1.7578125, 1.7617188, 1.765625, 1.7695312, 1.7734375, 1.7773438,
1.78125, 1.7851562, 1.7890625, 1.7929688, 1.796875, 1.8007812,
1.8046875, 1.8085938, 1.8125, 1.8164062, 1.8203125, 1.8242188,
1.828125, 1.8320312, 1.8359375, 1.8398438, 1.84375, 1.8476562,
1.8515625, 1.8554688, 1.859375, 1.8632812, 1.8671875, 1.8710938,
1.875, 1.8789062, 1.8828125, 1.8867188, 1.890625, 1.8945312,
1.8984375, 1.9023438, 1.90625, 1.9101562, 1.9140625, 1.9179688,
1.921875, 1.9257812, 1.9296875, 1.9335938, 1.9375, 1.9414062,
1.9453125, 1.9492188, 1.953125, 1.9570312, 1.9609375, 1.9648438,
1.96875, 1.9726562, 1.9765625, 1.9804688, 1.984375, 1.9882812,
1.9921875, 1.9960938, 2, 2.0039062, 2.0078125, 2.0117188, 2.015625,
2.0195312, 2.0234375, 2.0273438, 2.03125, 2.0351562, 2.0390625,
2.0429688, 2.046875, 2.0507812, 2.0546875, 2.0585938, 2.0625,
2.0664062, 2.0703125, 2.0742188, 2.078125, 2.0820312, 2.0859375,
2.0898438, 2.09375, 2.0976562, 2.1015625, 2.1054688, 2.109375,
2.1132812, 2.1171875, 2.1210938, 2.125, 2.1289062, 2.1328125,
2.1367188, 2.140625, 2.1445312, 2.1484375, 2.1523438, 2.15625,
2.1601562, 2.1640625, 2.1679688, 2.171875, 2.1757812, 2.1796875,
2.1835938, 2.1875, 2.1914062, 2.1953125, 2.1992188, 2.203125,
2.2070312, 2.2109375, 2.2148438, 2.21875, 2.2226562, 2.2265625,
2.2304688, 2.234375, 2.2382812, 2.2421875, 2.2460938, 2.25, 2.2539062,
2.2578125, 2.2617188, 2.265625, 2.2695312, 2.2734375, 2.2773438,
2.28125, 2.2851562, 2.2890625, 2.2929688, 2.296875, 2.3007812,
2.3046875, 2.3085938, 2.3125, 2.3164062, 2.3203125, 2.3242188,
2.328125, 2.3320312, 2.3359375, 2.3398438, 2.34375, 2.3476562,
2.3515625, 2.3554688, 2.359375, 2.3632812, 2.3671875, 2.3710938,
2.375, 2.3789062, 2.3828125, 2.3867188, 2.390625, 2.3945312,
2.3984375, 2.4023438, 2.40625, 2.4101562, 2.4140625, 2.4179688,
2.421875, 2.4257812, 2.4296875, 2.4335938, 2.4375, 2.4414062,
2.4453125, 2.4492188, 2.453125, 2.4570312, 2.4609375, 2.4648438,
2.46875, 2.4726562, 2.4765625, 2.4804688, 2.484375, 2.4882812,
2.4921875, 2.4960938, 2.5, 2.5039062, 2.5078125, 2.5117188, 2.515625,
2.5195312, 2.5234375, 2.5273438, 2.53125, 2.5351562, 2.5390625,
2.5429688, 2.546875, 2.5507812, 2.5546875, 2.5585938, 2.5625,
2.5664062, 2.5703125, 2.5742188, 2.578125, 2.5820312, 2.5859375,
2.5898438, 2.59375, 2.5976562, 2.6015625, 2.6054688, 2.609375,
2.6132812, 2.6171875, 2.6210938, 2.625, 2.6289062, 2.6328125,
2.6367188, 2.640625, 2.6445312, 2.6484375, 2.6523438, 2.65625,
2.6601562, 2.6640625, 2.6679688, 2.671875, 2.6757812, 2.6796875,
2.6835938, 2.6875, 2.6914062, 2.6953125, 2.6992188, 2.703125,
2.7070312, 2.7109375, 2.7148438, 2.71875, 2.7226562, 2.7265625,
2.7304688, 2.734375, 2.7382812, 2.7421875, 2.7460938, 2.75, 2.7539062,
2.7578125, 2.7617188, 2.765625, 2.7695312, 2.7734375, 2.7773438,
2.78125, 2.7851562, 2.7890625, 2.7929688, 2.796875, 2.8007812,
2.8046875, 2.8085938, 2.8125, 2.8164062, 2.8203125, 2.8242188,
2.828125, 2.8320312, 2.8359375, 2.8398438, 2.84375, 2.8476562,
2.8515625, 2.8554688, 2.859375, 2.8632812, 2.8671875, 2.8710938,
2.875, 2.8789062, 2.8828125, 2.8867188, 2.890625, 2.8945312,
2.8984375, 2.9023438, 2.90625, 2.9101562, 2.9140625, 2.9179688,
2.921875, 2.9257812, 2.9296875, 2.9335938, 2.9375, 2.9414062,
2.9453125, 2.9492188, 2.953125, 2.9570312, 2.9609375, 2.9648438,
2.96875, 2.9726562, 2.9765625, 2.9804688, 2.984375, 2.9882812,
2.9921875, 2.9960938, 3, 3.0039062, 3.0078125, 3.0117188, 3.015625,
3.0195312, 3.0234375, 3.0273438, 3.03125, 3.0351562, 3.0390625,
3.0429688, 3.046875, 3.0507812, 3.0546875, 3.0585938, 3.0625,
3.0664062, 3.0703125, 3.0742188, 3.078125, 3.0820312, 3.0859375,
3.0898438, 3.09375, 3.0976562, 3.1015625, 3.1054688, 3.109375,
3.1132812, 3.1171875, 3.1210938, 3.125, 3.1289062, 3.1328125,
3.1367188, 3.140625, 3.1445312, 3.1484375, 3.1523438, 3.15625,
3.1601562, 3.1640625, 3.1679688, 3.171875, 3.1757812, 3.1796875,
3.1835938, 3.1875, 3.1914062, 3.1953125, 3.1992188, 3.203125,
3.2070312, 3.2109375, 3.2148438, 3.21875, 3.2226562, 3.2265625,
3.2304688, 3.234375, 3.2382812, 3.2421875, 3.2460938, 3.25, 3.2539062,
3.2578125, 3.2617188, 3.265625, 3.2695312, 3.2734375, 3.2773438,
3.28125, 3.2851562, 3.2890625, 3.2929688, 3.296875, 3.3007812,
3.3046875, 3.3085938, 3.3125, 3.3164062, 3.3203125, 3.3242188,
3.328125, 3.3320312, 3.3359375, 3.3398438, 3.34375, 3.3476562,
3.3515625, 3.3554688, 3.359375, 3.3632812, 3.3671875, 3.3710938,
3.375, 3.3789062, 3.3828125, 3.3867188, 3.390625, 3.3945312,
3.3984375, 3.4023438, 3.40625, 3.4101562, 3.4140625, 3.4179688,
3.421875, 3.4257812, 3.4296875, 3.4335938, 3.4375, 3.4414062,
3.4453125, 3.4492188, 3.453125, 3.4570312, 3.4609375, 3.4648438,
3.46875, 3.4726562, 3.4765625, 3.4804688, 3.484375, 3.4882812,
3.4921875, 3.4960938, 3.5, 3.5039062, 3.5078125, 3.5117188, 3.515625,
3.5195312, 3.5234375, 3.5273438, 3.53125, 3.5351562, 3.5390625,
3.5429688, 3.546875, 3.5507812, 3.5546875, 3.5585938, 3.5625,
3.5664062, 3.5703125, 3.5742188, 3.578125, 3.5820312, 3.5859375,
3.5898438, 3.59375, 3.5976562, 3.6015625, 3.6054688, 3.609375,
3.6132812, 3.6171875, 3.6210938, 3.625, 3.6289062, 3.6328125,
3.6367188, 3.640625, 3.6445312, 3.6484375, 3.6523438, 3.65625,
3.6601562, 3.6640625, 3.6679688, 3.671875, 3.6757812, 3.6796875,
3.6835938, 3.6875, 3.6914062, 3.6953125, 3.6992188, 3.703125,
3.7070312, 3.7109375, 3.7148438, 3.71875, 3.7226562, 3.7265625,
3.7304688, 3.734375, 3.7382812, 3.7421875, 3.7460938, 3.75, 3.7539062,
3.7578125, 3.7617188, 3.765625, 3.7695312, 3.7734375, 3.7773438,
3.78125, 3.7851562, 3.7890625, 3.7929688, 3.796875, 3.8007812,
3.8046875, 3.8085938, 3.8125, 3.8164062, 3.8203125, 3.8242188,
3.828125, 3.8320312, 3.8359375, 3.8398438, 3.84375, 3.8476562,
3.8515625, 3.8554688, 3.859375, 3.8632812, 3.8671875, 3.8710938,
3.875, 3.8789062, 3.8828125, 3.8867188, 3.890625, 3.8945312,
3.8984375, 3.9023438), value = c(-335.332553252417, -325.579494919085,
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-349.107696307973, -342.029085196863, -350.976568530196, -353.200135196863,
-347.400669919085, -339.007418530196, -346.684894919084, -353.322094919084,
-349.685383807974, -342.477474085752, -344.592014363529, -353.41798797464,
-350.216621307973, -343.580340752418)), .Names = c("time", "value"
), row.names = c(NA, 1000L), class = "data.frame")
Raw
EMD.R
########
# This code comes from the EMD package (http://cran.r-project.org/web/packages/EMD/index.html) but incorporates optimizations that resulted from this thread: https://stat.ethz.ch/pipermail/r-help/2011-February/268550.html
########
diff1 <- function (x){
return(x[-1]-x[-length(x)])
}
rev1 <- function (x){
return(x[length(x):1L])
}
Sequence <- function(nvec) {
seq_len(sum(nvec)) - rep(cumsum(c(0L,nvec[-length(nvec)])), nvec)
}
isEndOfStrictlyIncreasingRun <- function(x) {
c(FALSE, diff1(diff1(x) > 0) < 0, FALSE)
}
isStartOfStrictlyIncreasingRun <- function(x) {
c(FALSE, diff1(diff1(x) <= 0) < 0, FALSE)
}
isEndOfStrictlyDecreasingRun <- function(x) {
isEndOfStrictlyIncreasingRun(-x)
}
isStartOfStrictlyDecreasingRun <- function(x) {
isStartOfStrictlyIncreasingRun(-x)
}
isStartOfZeroRun <- function(x) {
x==0 & c(TRUE, x[-length(x)]!=0)
}
nToEndOfCurrentFlatRun <- function(x) {
# for each element of x, how far to end of current
# run of equal values.
rev1( Sequence(rle(rev1(x))$lengths) - 1L)
}
indexOfEndOfCurrentFlatRun <- function(x) {
# should be a more direct way of doing this, but this is pretty quick
seq_len(length(x)) + nToEndOfCurrentFlatRun(x)
}
extrema <- function(x) {
f <- indexOfEndOfCurrentFlatRun(x)
isMaxStart <- isEndOfStrictlyIncreasingRun(x) & isStartOfStrictlyDecreasingRun(x)[f]
maxindex <- cbind(which(isMaxStart), f[isMaxStart], deparse.level=0)
isMinStart <- isEndOfStrictlyDecreasingRun(x) & isStartOfStrictlyIncreasingRun(x)[f]
minindex <- cbind(which(isMinStart), f[isMinStart], deparse.level=0)
# zero-crossings are bit weird: Report either the before-zero/after-zero
# pair or the start and stop of a a run of zero's (even if the run is
# not part of an actual zero-crossing). Do them separately then sort.
# Also, if the entire sequence never actually crosses 0, do
# not report the zero-touchings.
# Also, if length(x)==2, the original doesn't report a zero-crossing or
# a zero run. We do not copy that.
if (max(x) > 0 && min(x) < 0) {
indexOfZeroCrossingStart <- which(c(abs(diff1(sign(x)))==2,FALSE))
indexOfZeroCrossingEnd <- indexOfZeroCrossingStart + 1L
indexOfZeroRunStart <- which(isStartOfZeroRun(x))
indexOfZeroRunEnd <- f[indexOfZeroRunStart]
crossStart <- c(indexOfZeroCrossingStart, indexOfZeroRunStart)
cross <- cbind(crossStart, c(indexOfZeroCrossingEnd,indexOfZeroRunEnd), deparse.level=0)[order(crossStart),, drop=FALSE]
} else {
cross <- cbind(integer(), integer())
}
# extrema likes to return NULL instead of a zero-row matrix,
# so we follow it. Zero-row matrices are easier to deal with.
list(
minindex=if (nrow(minindex)) minindex else NULL,
maxindex=if (nrow(maxindex)) maxindex else NULL,
nextreme=nrow(minindex) + nrow(maxindex),
cross=if(nrow(cross)) cross else NULL,
ncross=nrow(cross) # extrema() returns numeric, not integer, here
)
}
emd <- function (xt, tt = NULL, tol = sd(xt) * 0.1^2, max.sift = 20,
stoprule = "type1", boundary = "periodic", smlevels = c(1),
sm = "none", spar = NA, weight = 20, check = FALSE, max.imf = 10,
plot.imf = TRUE, interm = NULL, print_progress = TRUE)
{
if (is.ts(xt))
xt <- as.numeric(xt)
if (is.null(tt))
tt <- 1:length(xt)
if (is.null(interm) || all(interm <= 0))
intermtest <- FALSE
else intermtest <- TRUE
ndata <- length(xt)
ndatam1 <- ndata - 1
residue <- xt
rangext <- range(residue)
imf <- NULL
j <- 1
repeat {
if (j > max.imf)
break
if ((any(j == smlevels) || smlevels == "all") & sm ==
"spline")
tmp <- extractimf(residue, tt, tol, max.sift, stoprule = stoprule,
boundary = boundary, sm = sm, spar = spar, check = check)
else tmp <- extractimf(residue, tt, tol, max.sift, stoprule = stoprule,
boundary = boundary, check = check)
if (is.null(tmp$imf)) {
break
}
if (plot.imf) {
plot(tt, residue, type = "l", xlab = "", ylab = "",
main = paste(j - 1, "-th residue=", j, "-th imf+",
j, "-th residue", sep = ""))
abline(h = 0)
}
if (intermtest && length(interm) >= j && interm[j] >
0) {
tmpimf <- tmp$imf
tmpresidue <- tmp$residue
tmpinterm <- extrema(tmpimf)
tmpncross <- tmpinterm$ncross
zerocross <- as.numeric(round(apply(tmpinterm$cross,
1, mean)))
if (abs(tt[zerocross[3]] - tt[zerocross[1]]) > interm[j]) {
tmpresidue[1:zerocross[3]] <- tmpresidue[1:zerocross[3]] +
tmpimf[1:zerocross[3]]
tmpimf[1:zerocross[3]] <- 0
}
for (k in seq(3, tmpncross - 3, by = 2)) if (abs(tt[zerocross[k +
2]] - tt[zerocross[k]]) > interm[j]) {
tmpresidue[zerocross[k]:zerocross[k + 2]] <- tmpresidue[zerocross[k]:zerocross[k +
2]] + tmpimf[zerocross[k]:zerocross[k + 2]]
tmpimf[zerocross[k]:zerocross[k + 2]] <- 0
}
if (!(tmpncross%%2)) {
if (abs(tt[zerocross[tmpncross]] - tt[zerocross[tmpncross -
1]]) > interm[j]/2) {
tmpresidue[zerocross[tmpncross - 1]:ndata] <- tmpresidue[zerocross[tmpncross -
1]:ndata] + tmpimf[zerocross[tmpncross -
1]:ndata]
tmpimf[zerocross[tmpncross - 1]:ndata] <- 0
}
}
else {
if (abs(tt[zerocross[tmpncross]] - tt[zerocross[tmpncross -
2]]) > interm[j])
tmpresidue[zerocross[tmpncross - 2]:ndata] <- tmpresidue[zerocross[tmpncross -
2]:ndata] + tmpimf[zerocross[tmpncross -
2]:ndata]
tmpimf[zerocross[tmpncross - 2]:ndata] <- 0
}
tmp$imf <- tmpimf
tmp$residue <- tmpresidue
}
imf <- cbind(imf, tmp$imf)
residue <- tmp$residue
if (plot.imf) {
plot(tt, imf[, j], type = "l", xlab = "", ylab = "",
main = paste(j, "-th imf", sep = ""))
abline(h = 0)
plot(tt, residue, type = "l", xlab = "", ylab = "",
main = paste(j, "-th residue", sep = ""))
abline(h = 0)
locator(1)
}
if(print_progress){
print(paste('IMF #',j,' obtained.',sep=''))
}
j <- j + 1
}
list(imf = imf, residue = residue, nimf = j - 1)
}
extractimf <- function (residue, tt = NULL, tol = sd(residue) * 0.1^2, max.sift = 20,
stoprule = "type1", boundary = "periodic", sm = "none", spar = NA,
weight = 20, check = FALSE)
{
if (boundary == "none")
minextrema <- 4
else minextrema <- 2
if (sm == "spline" & is.null(spar))
stop("Provide the smoothing parameter of spline smoothing.\n")
ndata <- length(residue)
ndatam1 <- ndata - 1
if (is.ts(residue))
residue <- as.numeric(residue)
if (is.null(tt))
tt <- 1:length(residue)
emin <- emax <- em <- h <- imf <- NULL
n2data <- 2 * ndata
tt2 <- 1:n2data
n3data <- 3 * ndata
n3datam1 <- n3data - 1
tt3 <- 1:n3data
input <- residue
rangext <- range(residue)
j <- 1
repeat {
tmp <- extrema(input)
if (tmp$nextreme <= minextrema)
break
if (j == 1 || boundary == "wave") {
minindex <- unique(c(t(tmp$minindex)))
minn <- length(minindex)
maxindex <- unique(c(t(tmp$maxindex)))
maxn <- length(maxindex)
extminindex <- minindex
extmaxindex <- maxindex
tmpwavefreq1 <- diff1(sort(c(tt[1], tt[minindex[1]],
tt[maxindex[1]])))
tmpwavefreq2 <- diff1(sort(c(tt[minindex[minn]], tt[maxindex[maxn]],
tt[ndata])))
if (input[1] <= input[minindex[1]] && input[1] <=
input[maxindex[1]]) {
extminindex <- c(1, extminindex)
wavefreq1 <- 2 * tmpwavefreq1[1]
}
else if (input[1] >= input[minindex[1]] && input[1] >=
input[maxindex[1]]) {
extmaxindex <- c(1, extmaxindex)
wavefreq1 <- 2 * tmpwavefreq1[1]
}
else if (input[1] >= (input[minindex[1]] + input[maxindex[1]])/2) {
wavefreq1 <- tmpwavefreq1[2] + max(tmpwavefreq1[2],
2 * tmpwavefreq1[1])
}
else {
wavefreq1 <- tmpwavefreq1[2] + max(tmpwavefreq1[2],
round(1.5 * tmpwavefreq1[1]))
}
if (input[ndata] <= input[minindex[minn]] && input[ndata] <=
input[maxindex[maxn]]) {
extminindex <- c(extminindex, ndata)
wavefreq2 <- 2 * tmpwavefreq2[2]
}
else if (input[ndata] >= input[minindex[minn]] &&
input[ndata] >= input[maxindex[maxn]]) {
extmaxindex <- c(extmaxindex, ndata)
wavefreq2 <- 2 * tmpwavefreq2[2]
}
else if (input[ndata] >= (input[minindex[minn]] +
input[maxindex[maxn]])/2) {
wavefreq2 <- tmpwavefreq2[1] + max(tmpwavefreq2[1],
2 * tmpwavefreq2[2])
}
else {
wavefreq2 <- tmpwavefreq2[1] + max(tmpwavefreq2[1],
round(1.5 * tmpwavefreq2[2]))
}
extminn <- length(extminindex)
extmaxn <- length(extmaxindex)
extttminindex <- c(tt[extminindex[1]] - 4:1 * wavefreq1,
tt[extminindex], tt[extminindex[extminn]] + 1:4 *
wavefreq2)
extttmaxindex <- c(tt[extmaxindex[1]] - 4:1 * wavefreq1,
tt[extmaxindex], tt[extmaxindex[extmaxn]] + 1:4 *
wavefreq2)
if (sm == "none") {
f <- splinefun(extttminindex, c(rep(input[extminindex[1]],
4), input[extminindex], rep(input[extminindex[extminn]],
4)))
emin <- cbind(emin, f(tt))
f <- splinefun(extttmaxindex, c(rep(input[extmaxindex[1]],
4), input[extmaxindex], rep(input[extmaxindex[extmaxn]],
4)))
emax <- cbind(emax, f(tt))
}
else if (sm == "spline") {
f <- sreg(extttminindex, c(rep(input[extminindex[1]],
4), input[extminindex], rep(input[extminindex[extminn]],
4)), lambda = spar)
llambda <- f$lambda * weight
f <- sreg(extttminindex, c(rep(input[extminindex[1]],
4), input[extminindex], rep(input[extminindex[extminn]],
4)), lambda = llambda)
llambda <- f$lambda
emin <- cbind(emin, predict(f, tt))
f <- sreg(extttmaxindex, c(rep(input[extmaxindex[1]],
4), input[extmaxindex], rep(input[extmaxindex[extmaxn]],
4)), lambda = spar)
ulambda <- f$lambda * weight
f <- sreg(extttmaxindex, c(rep(input[extmaxindex[1]],
4), input[extmaxindex], rep(input[extmaxindex[extmaxn]],
4)), lambda = ulambda)
ulambda <- f$lambda
emax <- cbind(emax, predict(f, tt))
}
em <- cbind(em, (emin[, j] + emax[, j])/2)
if (check) {
plot(tt, input, type = "l", col = 3, xlab = "",
ylab = "", main = paste("Boundary = ", boundary,
sep = ""))
points(tt[unique(c(t(tmp$minindex)))], input[unique(c(t(tmp$minindex)))],
col = 4)
points(tt[unique(c(t(tmp$maxindex)))], input[unique(c(t(tmp$maxindex)))],
col = 2)
lines(tt, emin[, j], col = 4)
lines(tt, emax[, j], col = 2)
lines(tt, em[, j])
locator(1)
}
}
else {
if (boundary == "none") {
minindex <- c(1, unique(c(t(tmp$minindex))),
ndata)
maxindex <- c(1, unique(c(t(tmp$maxindex))),
ndata)
fmin <- splinefun(tt[minindex], input[minindex])
emin <- cbind(emin, fmin(tt))
fmax <- splinefun(tt[maxindex], input[maxindex])
emax <- cbind(emax, fmax(tt))
em <- cbind(em, (emin[, j] + emax[, j])/2)
if (check) {
plot(tt, input, type = "l", col = 3, xlab = "",
ylab = "", main = paste("Boundary = ", boundary,
sep = ""))
points(tt[unique(c(t(tmp$minindex)))], input[unique(c(t(tmp$minindex)))],
col = 4)
points(tt[unique(c(t(tmp$maxindex)))], input[unique(c(t(tmp$maxindex)))],
col = 2)
lines(tt, emin[, j], col = 4)
lines(tt, emax[, j], col = 2)
lines(tt, em[, j])
locator(1)
}
}
if (boundary == "symmetric" || boundary == "periodic") {
if (boundary == "symmetric") {
inputext <- c(rev1(input[-1]), input, rev1(input)[-1])
ttext <- c(tt[1] - rev1(cumsum(diff1(tt))), tt,
tt[ndata] + cumsum(rev1(diff1(tt))))
tmp <- extrema(inputext)
}
else if (boundary == "periodic") {
inputext <- c(input[-ndata], input, input[-1])
ttext <- c(rev1(tt[1] - cumsum(rev1(diff1(tt)))),
tt, tt[ndata] + cumsum(diff1(tt)))
tmp <- extrema(inputext)
}
minindex <- unique(c(t(tmp$minindex)))
maxindex <- unique(c(t(tmp$maxindex)))
if (sm == "none") {
fmin <- splinefun(ttext[minindex], inputext[minindex])
tmpmin <- fmin(ttext[ndata:(n2data - 1)])
fmax <- splinefun(ttext[maxindex], inputext[maxindex])
tmpmax <- fmax(ttext[ndata:(n2data - 1)])
}
else if (sm == "spline") {
fmin <- sreg(ttext[minindex], inputext[minindex],
lambda = spar)
llambda <- fmin$lambda * weight
fmin <- sreg(ttext[minindex], inputext[minindex],
lambda = llambda)
tmpmin <- predict(fmin, ttext[ndata:(n2data -
1)])
fmax <- sreg(ttext[maxindex], inputext[maxindex],
lambda = spar)
ulambda <- fmax$lambda * weight
fmax <- sreg(ttext[maxindex], inputext[maxindex],
lambda = ulambda)
tmpmax <- predict(fmax, ttext[ndata:(n2data -
1)])
}
emin <- cbind(emin, tmpmin[1:ndata])
emax <- cbind(emax, tmpmax[1:ndata])
tmpm <- (tmpmin + tmpmax)/2
em <- cbind(em, tmpm[1:ndata])
if (check) {
plot(ttext[ndata:(n2data - 1)], inputext[ndata:(n2data -
1)], type = "l", col = 3, xlab = "", ylab = "",
main = paste("Boundary = ", boundary, sep = ""))
points(ttext[minindex[minindex > ndata & minindex <
n2data]], inputext[minindex[minindex > ndata &
minindex < n2data]], col = 4)
points(ttext[maxindex[maxindex > ndata & maxindex <
n2data]], inputext[maxindex[maxindex > ndata &
maxindex < n2data]], col = 2)
lines(ttext[ndata:(n2data - 1)], tmpmin, col = 4)
lines(ttext[ndata:(n2data - 1)], tmpmax, col = 2)
lines(ttext[ndata:(n2data - 1)], tmpm)
locator(1)
}
}
else if (boundary == "evenodd") {
inputeven <- c(input, rev1(input), input)
ttext <- c(tt, tt[ndata] + tt[ndata] - tt[ndatam1],
tt[ndata] + tt[ndata] - tt[ndatam1] + cumsum(rev1(diff1(tt))))
tmp <- extrema(inputeven)
minindex <- unique(c(t(tmp$minindex)))
minindex <- minindex[minindex <= n2data + minindex[1]]
maxindex <- unique(c(t(tmp$maxindex)))
maxindex <- maxindex[maxindex <= n2data + maxindex[1]]
if (sm == "none") {
f <- splinefun(ttext[minindex], inputeven[minindex])
emineven <- f(ttext[1:ndata])
f <- splinefun(ttext[maxindex], inputeven[maxindex])
emaxeven <- f(ttext[1:ndata])
}
else if (sm == "spline") {
fmin <- sreg(ttext[minindex], inputeven[minindex],
lambda = spar)
llambda <- fmin$lambda * weight
fmin <- sreg(ttext[minindex], inputeven[minindex],
lambda = llambda)
emineven <- predict(fmin, ttext[1:ndata])
fmax <- sreg(ttext[maxindex], inputext[maxindex],
lambda = spar)
ulambda <- fmax$lambda * weight
fmax <- sreg(ttext[maxindex], inputext[maxindex],
lambda = ulambda)
emaxeven <- predict(fmax, ttext[1:ndata])
}
inputodd <- c(input, -rev1(input), input)
tmp <- extrema(inputodd)
minindex <- unique(c(t(tmp$minindex)))
minindex <- minindex[minindex <= n2data + minindex[1]]
maxindex <- unique(c(t(tmp$maxindex)))
maxindex <- maxindex[maxindex <= n2data + maxindex[1]]
if (sm == "none") {
f <- splinefun(ttext[minindex], inputodd[minindex])
eminodd <- f(ttext[1:ndata])
f <- splinefun(ttext[maxindex], inputodd[maxindex])
emaxodd <- f(ttext[1:ndata])
}
else if (sm == "spline") {
fmin <- sreg(ttext[minindex], inputodd[minindex],
lambda = spar)
llambda <- fmin$lambda * weight
fmin <- sreg(ttext[minindex], inputodd[minindex],
lambda = llambda)
eminodd <- predict(fmin, ttext[1:ndata])
fmax <- sreg(ttext[maxindex], inputext[maxindex],
lambda = spar)
ulambda <- fmax$lambda * weight
fmax <- sreg(ttext[maxindex], inputext[maxindex],
lambda = ulambda)
emaxodd <- predict(fmax, ttext[1:ndata])
}
emin <- cbind(emin, (emineven + eminodd)/2)
emax <- cbind(emax, (emaxeven + emaxodd)/2)
em <- cbind(em, (emin[, j] + emax[, j])/2)
if (check) {
plot(tt, (inputeven + inputodd)/2[1:ndata],
type = "l", col = 3, ylab = "")
lines(tt, emin, col = 4)
lines(tt, emax, col = 2)
lines(tt, (emin + emax)/2)
locator(1)
}
}
}
h <- cbind(h, input - em[, j])
if (stoprule == "type1" && (all(abs(em[, j]) < tol) ||
j >= max.sift)) {
imf <- h[, j]
residue <- residue - imf
break
}
if (stoprule == "type2" && j >= 2) {
if (sum((h[2:ndatam1, j - 1] - h[2:ndatam1, j])^2/h[2:ndatam1,
j - 1]^2) < tol || j >= max.sift) {
imf <- h[, j]
residue <- residue - imf
break
}
}
input <- h[, j]
j <- j + 1
}
if (check)
list(emin = emin, emax = emax, em = em, h = h, imf = imf,
residue = residue, niter = j)
else list(imf = imf, residue = residue, niter = j)
}
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