I’ve tried to compare relative velocity variation (dvv) with Ground water(GW) variation. When I do fft, dvv shows reasonable data but GW shows only nan value. GW value’s average is 13, so I normalized them between -1 to 1 but it still shows nan values. I wonder how to get fft… About the value of ..
I have the following time series: D = [0.3, 0.4, 0.1,…] I need to fit D with: Fourier with 2 variables Where phi_1 and phi_2 are time series as well. Numpy.fft works only with one variable, how can I do this in python? Source: Python..
I am using scipy.fft module for calculating Fourier transformation of an array. Now using the module I got the transformation. But my question is how do I know the elements in the transformed array corresponds to which frequency. In short, the module scipy.fft.fft takes an array as input, how it knows that at which frequencies ..
I have the following dataset in normal space, lets call it func: I transformed it to fourierspace using the numpy fft algorithm from numpy.fft import fft as fourier, I received the fouriertransform usingfunc_fourier = np.fft.fftshift(fourier(func)) and plotted the absolute values plt.plot(np.abs(func_fourier)), what results in the following plot:. I now want to fit a gaussian model ..
After reading this excellent answer, which demonstrates the scaling of an FFT-based power spectral density (PSD) for real-valued data vs one computed via scipy.signal.welch, I wondered what difference it would make if the input time domain data were complex. I directly copied the code in the above answer, including the scaling factor ("scale"), and simply ..
I’m wondering if it is possible to smooth the estimated response from a Wiener deconvolution in order to have a better representation of the original signal and to remove the side lobes. (Here an example for an step signal) The recovered signal has been estimated through the wiener deconvolution of the output signal and a ..
I have got the following function , the central peak is approximately gaussianlike. I used the numpy library for the FFT algorithm from numpy.fft import fft as fourier, ifft as ifourier and transformed my function into fourierspacefunc_fourier = fourier(func), I expected a gaussianlike function in fourier space aswell, but I got this result, while plotting ..
I followed the example from https://pytorch.org/docs/stable/generated/torch.fft.fftshift.html#torch.fft.fftshift import torch.fft f = torch.fft.fftfreq(4) a = torch.fft.fftshift(f) print(a) and got the error AttributeError: module ‘torch.fft’ has no attribute ‘fftfreq’ I tried pip torch==1.7.0+cu110 and pip torch==1.7.1+cu110 and also conda pytorch==1.7.1 with cudatoolkit=11.0. Others have the same problem https://discuss.pytorch.org/t/unable-to-use-correctly-the-new-torch-fft-module/104560/6 But changing to torch1.7.0 didn’t solve the problem. How to ..
I made a sine wave by combining 2 other sine waves and some noise, than made the FFT for it. I looked online but I can’t find info on how to get the exact power (y value) that corresponds to a certain x value. To be more exact I want to get the exact power ..
I want to know what is wrong with the code. I just want to made a fourier transform graph and change the values by sliders. But this is what happened to my graph. enter image description here I just want to made a Parametric EQ graph interface like this, only the graph part with the ..