By cutting out a piece of signal for your analysis your essentially multiplying by a rectangular window function which will convolute with the signal one. With Fourier transforms a multiplication of two terms in the time domain is equivalent to the convolution of the spectra of both terms in the frequency domain. You could make an approximation by using lots of values - something like. Linspace assigns one value to each position, so you won't get the vertical line you want. To make the plot exactly you need to have two u values for t1, hence the way I defined it. Since you are not actually adding any useful data it’s more of an interpolation though.Įdit: A more complete answer is that you are also suffering from spectral leakage. u 1 1 0 0 0 plot (t, u, 'k-') which gets the plot you want. lin en el nombre linspace se refiere a la generacin de valores espaciados linealmente en contraposicin a la funcin hermana logspace, que genera. linspace es similar al operador de dos puntos, :, pero da un control directo sobre el nmero de puntos y siempre incluye los puntos finales. It is similar to the colon operator ':', but gives direct control over the number of points. y linspace(x1,x2,n) genera n puntos.El espacio entre los puntos es (x2-x1)/(n-1). If that is not feasible in your application you could try to increase the apparent resolution by zero padding the signal. linspace (MATLAB Functions) Generate linearly spaced vectors Syntax y linspace (a,b) y linspace (a,b,n) Description The linspace function generates linearly spaced vectors. The more data you have the greater your resolution of the spectrum becomes.
The easiest fix is to just collect data over a longer timespan. As you said you know it should be 1.0 but there is no „point“ there. Is it part of some module/library/package thanks. You basically have discretization errors. Linspace in python so I recently saw a post use linspace in python, is this not a MATLAB function I cannot find it in the docs and was under the impression that the python alternative for linspace is range. If I’m understanding you correctly you are testing with a signal of a known frequency and the peak frequency you get from the fft is slightly off so when you try to reconstruct the original signal by inverting the fft you get too large of a difference to the original signal? Well this doesn’t really have anything to do with linspace itself does it? It’s more of an issue with DFT.
0 kommentar(er)
