wavelet-transform | 易学教程

2D wavelet filtering in python on an image

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问题 I'm trying to do 2d wavelet filtering in python. I found out about PyWavelets and I have been messing around with it for awhile. I am trying to do the transformation for 4 levels. When I print it out it gives werid output and I'm not sure exactly what is going on. I've tried a few things but this is the latest as an example: test = pywt.dwt2(picture,'db1') Any help on preforming 2d wavelets on images with PyWavelets or just in general would be much appreciated. Thank you. Edit: The type of

2D wavelet filtering in python on an image

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why do we need time sampling to plot a stationary signal?

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问题 I am new to matlab and signal processing. I have wrote the below posted code. what i could not understand is, the time soecification section at the beginning of the code. I do not why do we need sampling when specifying an interval or time duration, i think it suffice to specify something like the following : t = (0: 0.2: 1.0) for an example, why do i need some thing like sampling to plot such as stationary signal. another question is, this code gives me an error saying paranthesis imbalance

The range of the coefficients of 2 level Wavelet LeGall 5/3 2D transform

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问题 As in the title, I am confused about the range of the coefficients of Wavelet LeGall 5/3 (has to be exact this filter) 2D transform (only for a 8*8 block) if the value of the input matrix are within the range from 0-255. For the formulas, the link is here: Wavelet LeGall 5/3 Here is what I did for now: Minus 128 for all value (easier to calculate the low frequency values, see later); Do the transform in horizontal direction. This will generate all coefficients in all lines: the first 4 are

R Shiny error: argument “x” is missing, with no default

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问题 I'm trying to make a model for non parametric regression with Maximal Overlap Discrete Wavelet Transform (MODWT) using this data taken from here and here. The packages I'm using here are shiny , wavelets , and readxl . Here is my server code: server <- function(input, output) { dataset<-reactive({ inFile <- input$filewav dat <- read_excel(inFile$datapath) df <- data.frame(dat[,2], dat[,3]) x <- seq(1,nrow(df),length = nrow(df)) y <- df[,2] return(dat) }) output$plot_mra <- renderPlot({ filt <

Is this wavelet transform implementation correct?

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问题 I am searching for alternatives to the FFT to create a spectrogram analyser in python. I heard that the wavelet transform is faster and provides better time accuracy than the short time FFT. I went in this wikipedia article that features the Haar wavelet transform implementation in Java: https://en.wikipedia.org/wiki/Discrete_wavelet_transform#Code_example I brutally converted it to python but I have no idea if the values I'm getting are correct. Can someone confirm? from math import * N = 8

Continuous Wavelet Transform with Scipy.signal (Python): what is parameter “widths” in cwt() function? (time-frequency)

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问题 I search to draw a time-frequency signal with a discrete temporal signal (sampling step = 0.001sec). I use Python and the library Scipy.signal. I use the function cwt(data, wavelet, widths), which returns a matrix, to do a continuous wavelet transform, with the complex morlet wavelet (or gabor wavelet). Unfortunately, there is not a lot of documentations of this use. The best which I found are: - this for Matlab (I try to find the same scale-time result) but I have naturally not access to the

Units of “widths” argument to scipy.signal.cwt() function

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问题 I am confused about the widths parameter that gets passed to scipy.signal.cwt() and by extension to scipy.signal.find_peaks_cwt() . A previous and very helpful Stack Overflow question (and pointers therein) explained away most of my confusion. The widths is an array of scales by which to stretch the wavelet before convolution with your data. The point which confused me still is, what are the units of the elements of widths ? Does a width of 1 mean the wavelet is stretched to be one "index"

Is this wavelet transform implementation correct?

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I am searching for alternatives to the FFT to create a spectrogram analyser in python. I heard that the wavelet transform is faster and provides better time accuracy than the short time FFT. I went in this wikipedia article that features the Haar wavelet transform implementation in Java: https://en.wikipedia.org/wiki/Discrete_wavelet_transform#Code_example I brutally converted it to python but I have no idea if the values I'm getting are correct. Can someone confirm? from math import * N = 8 res = [sin(k) for k in xrange(N)] for k in xrange(N): print res[k] print def

Units of “widths” argument to scipy.signal.cwt() function

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I am confused about the widths parameter that gets passed to scipy.signal.cwt() and by extension to scipy.signal.find_peaks_cwt() . A previous and very helpful Stack Overflow question (and pointers therein) explained away most of my confusion. The widths is an array of scales by which to stretch the wavelet before convolution with your data. The point which confused me still is, what are the units of the elements of widths ? Does a width of 1 mean the wavelet is stretched to be one "index" wide, where index is the distance between elements of data ? At first I assumed this was the case, but (a