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kandi X-RAY | wavelet Summary
wavelet is an open ledger for scalable, mission-critical, decentralized WebAssembly applications. Wavelet's very first protocol specification was written by Kenta Iwasaki and Heyang Zhou. This repository serves as Wavelet's initial Go implementation written by the Perlin team. Refer to the docs located here to learn how to locally setup your own three-node cluster, to learn how to write and deploy your very first WebAssembly smart contract, or to learn how Wavelet actually works.
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QUESTION
hope you're all fine.
That's my first question, so I'm sorry if something's not right about it.
I'm studying numerical stability and chaoticity of some dynamical systems, more specifically, about the Circular Restricted Three Body Problem (CR3BP), defined by the set of 3 second order differential equations. After transforming those three second order differential equations in six first order differential equations as seen here i can finally finally work with them numerically using scipy's ODEINT. Here's an example of an orbit integrated for T = 2^10 with n = 2^18 points (np.linspace(1, 2^10, 2^18)) and here's a bit of my code for this, the main function to be integrated:
...ANSWER
Answered 2021-May-28 at 07:13This is probably due to the step size control being also influenced by the rapidly growing v vector. Either by regulating step sizes rapidly down due to stiffness, or more likely, due to increasing the step size to match the dominant components, thus becoming unsuitable for an exact integration of the original trajectory. This rapid growth is the reason that Lyapunov exponents were introduced, as they capture this growth in nicely bounded numbers.
What you can do is to split up the integration into smaller chunks and normalize the v vector at the start of each chunk. One would have to experiment on how long it takes until the v component unduly dominates the step size control. As the coupling is purely multiplicative, the dynamic theoretically is linear. So it could also help if you scale the initial v to have norm 1e-100.
First however check what error tolerances you use. Setting them narrower also tends to stabilize the computation. You might also get some progress be setting the maximal step size hmax to half or so of the external step.
Or you could do the Lyapunov exponent computation like I explored in https://scicomp.stackexchange.com/questions/36013/numerical-computation-of-lyapunov-exponent. This approach however increases a system of dimension n by the n x n matrix of eigen/singular vectors and the n products of exponents times time.
QUESTION
I'm trying to implement the paper Integrated optimization of underwater acoustic ship-radiated noise recognition based on two-dimensional feature fusion.
My biggest problem is transforming .wav files to 2d arrays.
In the paper, it's mentioned
Each originally recorded signal in the database is framed using the hamming window of length 2048 with 50% overlap. With sampling frequency fs=52734 Hz, each sample lasts approximately 40 ms
and also
7 levels of WP decomposition with fourth-order Symlet wavelet is utilized to decompose each raw sample into 128 subbands.
I really appreciate any help in understanding and implementing this using Python.
...ANSWER
Answered 2021-May-21 at 14:06You can compute overlapping windows using librosa.utils.frame, with window_length=2048 and hop_length=1024 (50% overlap). The window function can be computed using librosa.filters.get_window with window="hamming"
Each window from the frame function you multiply by the window function. Then you can use pywt.wavedec with wavelet="sym" and level=7 to get the output for each frame. It should have 128 dimensions.
QUESTION
I applied a discrete wavelet transform to horizontal wind speed data to receive the below plot. I'm basically trying to use the information from the detail coefficient (the turbulent flow) for further analysis, but I'm not sure the best direction to go in. I don't have much experience with Wavelet Transform, so forgive me if there are obvious options, but the examples I've seen usually discard the higher frequency information since it's the noise of the signal. Is there anything further I can do with this discrete wavelet transform like statistic analysis or forecasting?
...ANSWER
Answered 2021-May-03 at 18:06The path to pursue really depends on the question that you are trying to answer.
First of all, I would suggest double checking that your DWT is actually doing what you expect it to do. The plot that you shared suggests that it is successful in separating the low frequency coherent (laminar?) flow from the high frequency turbulent flow, but it would be helpful to figure out which frequencies are present in the high frequency component in order to confirm that the processing parameters (e.g. decomposition level) were properly chosen.
Once convinced that your wavelet decomposition provides you with useful information about the turbulent flow, what should you do with these high pass filtered data? I suggest computing their variance over 1 hour long intervals. This is a measure of the "energy" of the signal over the chosen interval. If you are dealing with large amounts of data this would allow you to boil down your time series into a single sample per hour. Maybe you will be able to spot diurnal variations in the turbulent flow (e.g. maybe turbulent flow is higher at dawn). If you have multiple stations it would be interesting to study if the turbulence variations share the same behavior.
Before venturing into time series forecasting, I would really take a closer look at you data and try to identify trends or nail down possible outliers.
Last but not least, I would suggest posting your question on Physics Stack Exchange (e.g. https://physics.stackexchange.com/) rather than on SO.
QUESTION
As follows, my task is to use AR modeling to remove artifacts from noisy signals. Let's say I have ECG or EMG in raw data. On IEEE I have found that this is possible via Wavelet transform, Butterworth filters or Empirical mode decomposition.
https://www.kaggle.com/residentmario/denoising-algorithms#Machine-learning-models
Raw EMG:
What exactly am I supposted to do with Auto Regression model? As I understand it right now it is used to forecast the data.
...ANSWER
Answered 2021-Mar-25 at 17:04As I understand it right now it is used to forecast the data.
Yes, that's a common case for AR(p) models; but in order to forecast, its parameters should be estimated and it is done over the observations you provide to it. Therefore you can have so-called "fitted values" and use them as the "denoised" version of the signal at hand. This is because AR(p) is this:
QUESTION
I am trying to reuse this old function generated by a former employee and the company I now work at and am receiving the error "TypeError: object of type cannot be safely interpreted as an integer." I thought I could fix it quite easily with integer division // but that doesn't seem to be fixing the issue.
...ANSWER
Answered 2021-Mar-20 at 02:26You should be able to convert any float into int using
QUESTION
I have a dataset for banknotes wavelet data of genuine and forged banknotes with 2 features which are:
- X axis: Variance of Wavelet Transformed image
- Y axis: Skewness of Wavelet Transformed image
I run on this dataset K-means to identify 2 clusters of the data which are basically genuine and forged banknotes.
Now I have 3 questions:
- How can I count the data points of each cluster?
- How can I set a color of each data point based on it's cluster?
- How do I know without another feature in the data if the datapoint is genuine or forged? I know the data set has a "class" which shows 1 and 2 for genuine and forged but can I identify this without the "class" feature?
My code:
ANSWER
Answered 2021-Jan-14 at 12:55- How to count the data points of each cluster
You can do this easily by using fit_predict instead of fit, or calling predict on your training data after fitting it.
Here's a working example:
QUESTION
I've gotten >100 csv files, each containing >1000 measurements structured like the following two example files
MR44825_radiomics_MCA.csv
...ANSWER
Answered 2021-Jan-01 at 20:54CSV doesn't really make sense for the data you presented, and the pseudo-CSV you say you want as output makes little sense and will be hard to process further. Perhaps converting each input file to JSON would make more sense, and allow for processing using standard tools.
QUESTION
Trying to revive a PyUNO sample script called Wavelet to learn how LO works nowadays and get re-started. Since LibreOffice & UNO changed a bit from the script's creation time I am running into problems.
Managed to get the desktop object. Now I want to retrieve the open document's component. How do I achieve this properly? The desktop.getCurrentComponent() call returns None.
LibreOffice version: 6.4.6.2.
System: Ubuntu MATE 20.04 x86_64.
The code follows:
...ANSWER
Answered 2020-Dec-20 at 00:07Try without giving desktop.getCurrentComponent() a variable, so erase doc =. I remember I had that problem, but did not understand why it was doing it. All I remember is that not naming it made my code work. That is the only advice I can give you.
QUESTION
How to colour space between two lines with a colour transition? For instance, with grey colour - the dark grey from the upper line should become lighter as proceeding to the lower line. Thank you
...ANSWER
Answered 2020-Dec-15 at 11:18You could draw a lot of parallel lines (or curves) using a color from a gray-scale colormap. The example code below uses a transformation u = t/2 + 0.25, so when t goes from 0 to 1, u would just go between 0.25 and 0.75 to select of specific range from the colormap, avoiding the very dark and very light parts.
QUESTION
My goal is to apply wavelet analysis and image construction to large data set of time series data to be eventually used in pipeline for time series clustering. The function to do the first step is from WaveletComp and I am using purr map () from Tidyverse package. Ideally the output is a list labeled for each column that I can then apply other functions to in the pipeline.
...ANSWER
Answered 2020-Nov-24 at 11:02You could write a function which takes two input, dataframe and column name/position.
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