similar to: fft(x, inv=TRUE)

Hi, I'm in dire need of a fast fourier transformation for me stupid biologist, i.e. I have a heartbeat signal and would like to decompose it into pure sin waves, getting three vectors, one containing the frequencies of the sin waves, one the magnitudes and one the phases (that's what I get from my data acquisition software's FFT function). I'd be very much obliged, if someone

I read a tab-delimited ASCII table, with a header, into R thus. Table <- read.table(FileName, header=TRUE, sep="\t"); Is there any way I can now retrieve the header. The header information is obviously there since I see it, along with the values of the first row of data, when I type Table[1, ] However, I cannot find an R function to retrieve the header information from

I have a a simple function that generates a time series square wave: genseq <- function() { x <- numeric(4*365) s <- seq(as.Date("2005-01-01"), as.Date("2008-12-31"), by="month") ob <- as.vector(s[c(10,22,34,46)] - as.Date("2005-01-01")) oe <- as.vector(s[c(11,23,35,47)] - as.Date("2005-01-01")) for(.index in 1:length(ob))

Hello, I'm new to R (using it since about two weeks), but absolutely a fan of it from the beginning on. :-) Best tool for working with data I found. :-) I tried using the fft() and other funcitons for analysing time series. What I would be glad to have, would be a convenient way to display the complex result of a fft in a way, that real and imaginary parts each use an axis for themselves,

Hi R-users: I want to know if there is an easy way to obtain a Fourier Transform form a vector or an array (just like fft does), but with a more density base. I mean, if I have a vector of 512 of length, I want the Fourier Transform to be 1024, or 2048, etc, in length (de u domain). Or should I modify the fft C code to do that? If I want to modify the precision of the fft function, which

Hi! I am using the fft() function the base package to transform some 1d signal. If I use this standar fucntion I get a very huge first fourier coeficient. I think this dues to the handling of the borders of the signal. Usually in fft especially in image processing the signal is simulated to be continuous by adding the signal several times periodically. My question is, is there some function

The characteristic function is the inverse Fourier transform of the distribution function. The characteristic function of a normaly distributed random variable is exp(-t^2/2). x=seq(-2,2,length=100) fft(pnorm(x),inverse=T)/length(x) exp(-x^2/2) Why aren't the inverse fft and the mentioned function the same? Thanks for help, Thomas

Dear all, Before I can get into serious Fourier analysis of Radon time-series I am practising with 24hour and 24.8hour sinusoids to assist with my interpretation of signals ittributed to tidal input to Radon time series. I am stuck. I have tried researching this to no avail. I am awating a book that should describe fourier transforms in detail and another one that should discuss the R

Hello, I have calculated the fourier transform of the series enclosed at the end of this message, by doing: library(stats) x <- readLines("file1.txt") x.num <- as.numeric(x) ft.x.num <- fft(x.num) My question is: why is the first value (Real) of ft.x.num that big? (954.833870) all the other values are much smaller. Am I doing something wrong? Could you please help me to

Hi List, I'm trying to perform some spectral analysis on a time series, but I've got a few data points missing, so they have NA values. Unfortunately, the fft function doesn't seem to like this, and gives a completely empty result. Other than making up values to fill in the gaps, is there any way I can get around this? Thanks in advance, Steve. PS I haven't spotted anything

I wrote a script that I anticipating seeing a spike at 10Hz with the function 10* sin(2*pi*10*t). I can't figure out why my plots do not show spikes at the frequencies I expect. Am I doing something wrong or is my expectations wrong? require(stats) layout(matrix(c(1,2,3), 3, 1, byrow = TRUE)) #SETUP n <- 256 #nextn(1001) gives next power 2 F <- 100 #Hz -50 to 50 Hz dt

Hi, A very simple question. I know that the Fourier transform of a Laplace distribution, with zero mean and variance 2b^2, is equal to 1/(1+(tb)^2). Therefore, the Fourier transform is a positive function (actually is always between 0 and 1). BUT, if I use the fft(alpha) function of R, where alpha is a vector containing 128 values of the probability density function from -2 to 2, I get negative

R 1.4.1, Linux and Windows for(i in 1:1000) { X <- rnorm(i) XX <- fft(fft(X), inverse=T)/i if(max(Mod(XX-X)) > 1e-10) print(i) } [1] 392 [1] 588 [1] 968 [1] 980 and I then get a segfault during gc(). The answers are way off, with imaginary parts 1e10 or more. These numbers are all multiples of 7^2 or 11^2. (Based on a report to R-help Date: Thu, 28 Mar 2002 09:37:34

I am in the process of dropping the TI DSP lib fft routines into the AEC. Currently, FFTWrap.c uses the USE_SMALLFT and USE_KISS_FFT to select between two packages of FFT routines. ] #ifdef USE_SMALLFT ... #elif defined(USE_KISS_FFT) ... #else #error No other FFT implemented #endif My guess is that every DSP and many other processors will have their own specific FFT and iFFT. What method

This message is in MIME format --_=XFMail.1.3.p0.Linux:990422185944:4782=_ Content-Type: text/plain; charset=us-ascii R-0.64.0 on RedHat Linux 5.1 Some problems with multivariate fast fourier transform. I have attached the dump of a 30 x 3 matrix that seems to reliably reproduce these problems 1) mvfft doesn't like vectors. It complains about not having enough memory: R>

I am puzzled by a filtering problem using fft(). I don't blame R. I have a waveform y consisting of the sum of 2 sinewaves having freqs f1 and f2. I do s = fft() of y. Remove s's spike at freq=f2 Do inverse fft on s. The resulting waveform still has a lot of f2 in it! But the filtering should have removed it all. What is going on, and how to fix?? Thanks very much for any help. Bill

Dear R-ophiles, I've found something very odd when I apply convolve to ever larger vectors. Here is an example below with vectors ranging from 2^11 to 2^17. There is a funny bump up at 2^12. Then it gets very slow at 2^16. > for( i in 11:20 )print( system.time(convolve(1:2^i,1:2^i,type="o"))) user system elapsed 0.002 0.000 0.002 user system elapsed 0.373

Dear Experts, I want to know if the KISS FFT in opus can be replaced with fast FFT. What are the implications & things to be taken care if this is done. Also, I saw in FFT code comments that in-line FFT not supported. Please let me know the reason for this. Thanks in advance. Regards, Mahantesh -------------- next part -------------- An HTML attachment was scrubbed... URL:

Hi, I am working on DSP module of Ne10. I see there are fixed-point and floating-point FFT inside Opus. Is fixed-point FFT only a fall back for CPU without VFP? On ARMv7-A and ARMv8-A, benchmark result shows that fixed-point (int32) and floating-point (float32) FFT have similar performance. I guess fixed-point version is not often used on these platforms. Is it worth the effort to NEON-optimize

On 29 April 2015 at 17:22, Timothy B. Terriberry <tterribe at xiph.org> wrote: > > Viswanath Puttagunta wrote: >> >> This patch series is follow up on work I posted on [1]. >> In addition to what was posted on [1], this patch series mainly >> integrates Fixed point FFT implementations in NE10 library into opus. >> You can view my opus wip code at [2]. >