fft

问题 I used to use fftw_plan_dft for multi-dimensional Fourier transformation. fftw_plan fftw_plan_dft(int rank, const int *n, fftw_complex *in, fftw_complex *out, int sign, unsigned flags); Now I want to pass 64 bit integer to fftw, it looks like I need to use fftw guru interface. fftw_plan fftw_plan_guru64_dft( int rank, const fftw_iodim64 *dims, int howmany_rank, const fftw_iodim64 *howmany_dims, fftw_complex *in, fftw_complex *out, int sign, unsigned flags); But I do not understand what is

问题 I am trying to compute 3D convolution of a 3D array using Intel MKL . Could someone kindly give me some hints how I can do that? Is it achievable using MKL ? Thanks in advance. 回答1: Intel has an example on their page of a 3D FFT, which should be helpful for performing convolution by multiplication in frequency space. Sorry I don't have a full solution: Three-Dimensional REAL FFT (C Interface) #include "mkl_dfti.h" float x[32][100][19]; float _Complex y[32][100][10]; /* 10 = 19/2 + 1 */ DFTI

问题 I am testing drawing audio frequency into canvas using canvas.drawLine() method. I am able to do static draw into canvas. Basically I have a test app which has two buttons START and STOP and a Canvas where i am trying to draw the audio frequencies obtained from FFT. When i hit the START button it starts recording sound using AudioRecord class and collects data into buffer which i run through FFT to get the frequencies. This is in very early stage. Right now i am only trying to figure out how

问题 Trying to understand an fft (Fast Fourier Transform) routine I'm using (stealing)(recycling) Input is an array of 512 data points which are a sample waveform. Test data is generated into this array. fft transforms this array into frequency domain. Trying to understand relationship between freq, period, sample rate and position in fft array. I'll illustrate with examples: ======================================== Sample rate is 1000 samples/s. Generate a set of samples at 10Hz. Input array has

问题 I'm currently trying to understand the 2d fourier shift theorem. According to what I've learnd so far a translation in the image space leads to differences in phase but not the magnitude in frequency space. I tried to demonstrate this with a little example but it only worked for shifts in rows but not in columns. Here's the little demo (I'm only showing the magnitude plots here) clear all close all Iin = zeros(128); Iin(10:20,10:20)=1; figure,imagesc(Iin) Y = fft(Iin); figure, imagesc

问题 I've written a python code to take a 2D signal and FFT it, and now I want to extract the frequencies associated with the FFT. The np.fft.fftfreq fails, giving me the error File "/usr/lib64/python2.7/site-packages/numpy/fft/helper.py", line 153, in fftfreq assert isinstance(n,types.IntType) or isinstance(n, integer) AssertionError My code is : import numpy as np import scipy as sp import pylab import sys import math filename = sys.argv[1] # Get name of file to open ifp = open(filename, "r")

问题 If I am trying to do a convolution kernel in Frequency space - what is the "do-nothing" kernel. In other words, if I view the image after applying the kernel, and normalizing it in Frequency space, I just want to see the raw Fourier transform Is it the identity matrix? my kernel is 3x3 Thanks 回答1: A do-nothing 3x3 kernel will be: 0 0 0 0 1 0 0 0 0 I hope I understood your question correctly - I'm not sure why you would want such a kernel, when it's much easier to just skip the convolution

问题 The aim of this post is to properly understand Numerical Fourier Transform on Python or Matlab with an example in which the Analytical Fourier Transform is well known. For this purpose I choose the rectangular function, the analytical expression of it and its Fourier Transform are reported here https://en.wikipedia.org/wiki/Rectangular_function Here the code in Matlab x = -3 : 0.01 : 3; y = zeros(length(x)); y(200:400) = 1; ffty = fft(y); ffty = fftshift(ffty); plot(real(ffty)) And here the

问题 Whenever I'm plotting the values obtained by a programme using the cuFFT and comparing the results with that of Matlab, I'm getting the same shape of graphs and the values of maxima and minima are getting at the same points. However, the values resulting by the cuFFT are much greater than those resulting from Matlab. The Matlab code is fs = 1000; % sample freq D = [0:1:4]'; % pulse delay times t = 0 : 1/fs : 4000/fs; % signal evaluation time w = 0.5; % width of each pulse yp = pulstran(t,D,

问题 Whenever I'm plotting the values obtained by a programme using the cuFFT and comparing the results with that of Matlab, I'm getting the same shape of graphs and the values of maxima and minima are getting at the same points. However, the values resulting by the cuFFT are much greater than those resulting from Matlab. The Matlab code is fs = 1000; % sample freq D = [0:1:4]'; % pulse delay times t = 0 : 1/fs : 4000/fs; % signal evaluation time w = 0.5; % width of each pulse yp = pulstran(t,D,