Giter Site home page Giter Site logo

graetz23 / jwave Goto Github PK

View Code? Open in Web Editor NEW
213.0 21.0 75.0 1.87 MB

A Discrete Fourier Transform (DFT), a Fast Wavelet Transform (FWT), and a Wavelet Packet Transform (WPT) algorithm in 1-D, 2-D, and 3-D using normalized orthogonal (orthonormal) Haar, Coiflet, Daubechie, Legendre and normalized biorthognal wavelets in Java.

Home Page: http://en.wikipedia.org/wiki/Wavelet

License: Other

Java 100.00%
wavelet wavelet-transform wavelet-transform-algorithm wavelet-decomposition wavelet-compression wavelet-packets wavelet-analysis wavelet-toolbox wavelet-tree orthogonal

jwave's Introduction

JWave

Java library keeping orthogonal / orthonormal and bi-orthogonal wavelets

GitHub commit activity GitHub Repo stars GitHub forks

Introduction

Java implementation of a Discrete Fourier Transform (DFT), a Fast Wavelet Transform (FWT), and a Wavelet Packet Transform (WPT) algorithm. All algorithms are available in 1-D, 2-D, and 3-D. The wavelet transform algorithms are using normalized orthogonal or if available orthonormal wavelets. The comon wavelets like Haar, Coiflet, Daubechies, Symlets, and Legendre are available. Additionally there are also some Bi-Orthogonal and unusal wavelets implemented - in total around 50 wavelets.

The implementation of JWave is based on several software design patterns and - hopefully - appears therefore user-friendly.

GETTING STARTED

First steps

Have a look at the little how to down the page or try looking at the unit tests.

Can I perform odd samplings like, e.g. 127 data points?

However, the bare algorithms of JWave do only support data sampled by 2^p | p E N; e.g. 2, 4, 8, 16, .. 128, 256, 512, 1024, .. and so on. Please use the AncientEgyptianDecomposition class for odd samplings (most frequently asked question)! You can find it down the page.

Why are the results totally different then expected?!

Why do the results (hilbert spaces) look totally different to those from my matlab or some other implementation I found on net?!

In most cases, other libraries construct the orthogonal / orthonormal bases of the wavelet and scaling function in a different way. Especially for those bases of multiple dimension or wavelets of higher dimension, repectively. But does this hurt?

Totally not!

The why can be found in mathematics. Due to using some orthogonal transform (or better, an orthogonal base), it is up to oneself how to construct this base (as long it stays orthogonal over all dimensions). Next it is also up to oneself how to apply the sequence of the transform steps. Both does not influence any performance of the wavelet transforms! But again why?

The base stays orthogonal, and one's data is unit rotated and mirrowed differently, which makes a long story short.

Additionally the application of the transform - independent of using some different rotating and mirrowing base - is like dancing some 90's techno: As long as you do the same amount of steps independently of the performed sequence, even in 2-D and 3-D dimensions, the expected magic wavelets can bring in, will be there, and stays the same! For example, the result in values, and e.g the compression rates will stay exactely the same. Only all intermediate performed results (or intermediate hilbert spaces) will be different, if someone else dances differently to otherones.

HowTo

For a quick test, pull the repository and then: ant && ant test. This builds a JWave.jar and the corresponding unit tests. Afterwards all units test are executed.

Doing own stuff e.g. data compression

For a lot of own stuff with JWave, have a look at the main junit test file / method: a lot of examples!

For example, how to perform a (losless) data compression with over 98 % compression rate using all available wavelets is shown by the following junit test!

Some easy code ..

example for 1-D DFT:

Transform t = new Transform( new DiscreteFourierTransform( ) );

// arrTime = { r1, c1, r2, c2, ... }`
double[ ] arrTime = { 1., 1., 1., 1., 1., 1., 1., 1. };

double[ ] arrFreq = t.forward( arrTime ); // 1-D DFT forward

double[ ] arrReco = t.reverse( arrFreq ); // 1-D DFT reverse

example for 1-D, 2-D FWT:

Transform t = new Transform( new FastWaveletTransform( new Haar1( ) ) );

double[ ] arrTime = { 1., 1., 1., 1., 1., 1., 1., 1. };

double[ ] arrHilb = t.forward( arrTime ); // 1-D FWT Haar forward

double[ ] arrReco = t.reverse( arrHilb ); // 1-D FWT Haar reverse

double[ ][ ] matTime = { { 1., 1., 1., 1. },
                         { 1., 1., 1., 1. },
                         { 1., 1., 1., 1. },
                         { 1., 1., 1., 1. } };

double[ ][ ] matHilb = t.forward( matTime ); // 2-D FWT Haar forward

double[ ][ ] matReco = t.reverse( matHilb ); // 2-D FWT Haar reverse
// example in 3-D in common to 2-D using a N^3 double[ ][ ][ ] space.

example for 1-D, 2-D WPT:

Transform t = new Transform( new WaveletPacketTransform( new Haar1( ) ) );

double[ ] arrTime = { 1., 1., 1., 1., 1., 1., 1., 1. };

double[ ] arrHilb = t.forward( arrTime ); // 1-D WPT Haar forward

double[ ] arrReco = t.reverse( arrHilb ); // 1-D WPT Haar reverse

double[ ][ ] matTime = { { 1., 1., 1., 1. },
                         { 1., 1., 1., 1. },
                         { 1., 1., 1., 1. },
                         { 1., 1., 1., 1. } };

double[ ][ ] matHilb = t.forward( matTime ); // 2-D WPT Haar forward

double[ ][ ] matReco = t.reverse( matHilb ); // 2-D WPT Haar reverse

// example in 3-D in common to 2-D using a N^3 double[ ][ ][ ] space.

example for 1-D FWT of arbitrary length:

Transform t = new Transform(
               new AncientEgyptianDecomposition(
                new FastWaveletTransform(
                 new Haar1( ) ) ) );

double[ ] arrTime = { 1., 1., 1., 1., 1., 1., 1. }; // length = 7

double[ ] arrHilb = t.forward( arrTime ); // 1-D AED FWT Haar forward

//           |    2 steps    |   1 step   | 0  |
// arrHilb = { 2., 0., 0., 0., 1.41421, 0., 1. };
double[ ] arrReco = t.reverse( arrHilb ); // 1-D AED FWT Haar reverse

example for 1-D WPT (WPD) of arbitrary length:

Transform t = new Transform(
               new AncientEgyptianDecomposition(
                new WaveletPacketTransform(
                 new Haar1( ) ) ) );

double[ ] arrTime = { 1., 1., 1., 1., 1., 1., 1. }; // length = 7

double[ ] arrHilb = t.forward( arrTime ); // 1-D AED WPT Haar forward

double[ ] arrReco = t.reverse( arrHilb ); // 1-D AED WPT Haar reverse

Have fun! :-)

CONTACT

If there are doubts, try mailing me, otherwise have fun with JWave.

LICENSE

JWave is distributed under the MIT License (MIT); this file is part of.

Copyright (c) 2008-2024 Christian ([email protected])

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

VERSION

JWave is in version 200303.

CHANGE LOG

version 200303:

  • updating copyright and contact information to graetz23

version 180222:

  • updating project description, especially using markdown for the README.md,
  • updated JUnit imports and travis-ci.org configuration for continuous integration,

version 160218:

  • added a new type of wavelet transform algorithm: Shifting Wavelet Transform.
  • the algorithm shifts a wavelet by smallest length of 2 over the input array,
  • then by the double of 4, 8, 16, .., p-1, p.
  • the reverse transform takes the largest wavelength of p and shifts,
  • then by half of p-1, .., 16, 8, 4, 2.

version 160109:

  • moved the JUnit tests to an own source directory
  • updated the build.xml file
  • automatically run all files with regex **/*Test* in build directory
  • updated the build.xml - allowing for OS specific builds now
  • set JUnit path OS dependent; MAC, WIN, or GNU/Linux and Unix
  • additionally set for WIN OS org.hamcrest.core JAR

version 160107:

  • added junit test for compressing a sine signal:
  • sine signal of 1024 * 1024 samples by 1024 oscillations
  • calculating the compression rate in percent; e.g. 99.70703125 % by Daubechies 20 wavelet
  • calculating the absolute maximal differences; e.g. 3.086649707542688E-4 by Daubechies 20 wavelet
  • added method for calculating compression rate to class Compressor

version 160106:

  • added build.xml for using ant
  • set in build.xml your path JUnit4, e.g. /usr/share/java/junit.jar
  • ! to have the build done:
  • ant
  • should work easy peasy
  • ! for a quick console example:
  • ant run
  • ! for running the junit tests
  • ant test
  • ! to have a run or test on your system:
  • java -cp ./dist/JWave.jar jwave.JWave Fast Wavelet Transform Daubechies 20
  • java -cp /usr/share/java/junit4.jar:./dist/JWave.jar org.junit.runner.JUnitCore jwave.TransformTest
  • added automatic build using travis-ci.com
  • ! https://travis-ci.org/graetz23/JWave
  • have a look at ./.travis.yml
  • added README.md
  • added LICENSE.md
  • updated Copyright to the years 2008-2018
  • fixed bug in JWave.java for calling console example with Haar wavelet

jwave's People

Contributors

cscheiblich avatar dmcreyno avatar graetz23 avatar hbs avatar raineforest avatar

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

jwave's Issues

Arbitrary length wavelet coefficients order

Hello,

I have tried to understand the working of Ancient Egyptian Decomposition transform for wavelet transforms, but when trying to address the coefficients corresponding to a particular frequency band I am having difficulty in finding a pattern.

When we have a wavelet transform, it divides the coefficients in each level by 2 from the Low frequency (Approximate coefficients) copying detailed coefficients as it is in next level
Here it is easy to find a pattern as we know every time the approximate coefficients were divided in index by 2

But what would be the case when we are working with arbitrary length signals?

`

Wavelet demy =  WaveletBuilder.create( "Discrete Mayer" );
Transform fwt = new Transform(new AncientEgyptianDecomposition ( new FastWaveletTransform( demy ) ) );

int level_decomposed = 4;
double [] arrTime = getTimeSeriesData(); // returns an array of arbitrary length every time @30Hz

// These values for de-noising the signal only works for 2^p length signals
// Denoising to 0 - 3.75Hz 
int dcValuesP = pulse.length / (int) Math.pow(2.0, (double) level_decomposed);
int value_denoise = dcValuesP * 2;

double [] arrHilb = fwt.forward(arrTime);

for (int dp = value_denoise; dp < arrHilb.length; dp++) {
    arrHilb[dp] = 0.0D;
}

for (int i = 0; i < dcValuesP; i++) {
    arrHilb[i] = 0.0D;
}

double [] arrReco = fwt.reverse(arrHilb);
`

Could you help me in finding the pattern which would help me achieve similar denoising effect (0-30Hz to 0-3.75Hz using dmey wavelet) for arbitrary length time series data?

PS. I was working the same in Matlab there we get an array of index book keeping for a particular range of coefficients, if there is something similar to it, that would be great!

Example Usage doesn't work

Hi,

I'm trying to use your library and startetd with example "Example for 1-D, 2-D FWT" where you use a Haar02 Wavelet.

When I copy the code as described, the Wavelet Haar02 doesn't exist. So I tried with Haar1, which gave me no problems.
Also I had to surround 4 lines with try/catch block which throws JWaveExceptions.

So here are my questions:
Is Haar1 the same as Haar02 Wavelet?
Why do I need the try/catch blocks?

thanks for your help
Alex

Question: Structure of the coefficients in the array returned from the forward transform

Hi,

I’m looking to implement the image fusion algorithm, which requires combining the coefficients of the wavelet transform of two images, then calculating the inverse one the combination.

The combining operation requires treating the “approximation” and “detail” coefficients differently. However, it wasn’t obvious to me how to determine what sections of the result for the forward transform correspond to the approximation and detail coefficients.

@graetz23 Can you provide some guidance on the layout? Once I understand this I’d be happy to submit a PR so that others don’t have the same trouble I’ve had.

Use of arbitrary length input array to Daubechies 4 FWT

Very good implementation of Wavelet Transforms, just what I required for my OU project here in the UK.

However, I have a problem implementing a Fast Wavelet Transform on an arbitrary length array of data (as per your examples).

It appears I am invoking the parent forward method rather than the overriden one in the AncientEgyptianDecomposition class.

My Java is:

       double[] arrFwd = null;
        //instance of Daubechies D4 DWT
        d4Transform = new Transform(
                        new AncientEgyptianDecomposition(
                            new FastWaveletTransform(
                                new Daubechies4())));

       double[] frame = new double [1280];

        //code to populate frame array

        // should use overriden forward method from AncientEgyptianDecomposition class
        arrFwd = d4Transform.forward(frame);

Could this be because AncientEgyptianDecomposition extends BasicTransform and not Transform?

Any help would be appreciated.

Regards,

Kevin

Cannot build

Buildfile: //JWave/build.xml

clean:
[delete] Deleting directory //JWave/build

doMac:

doWin:

doUnix:
[echo] OS is a Unix target
[echo] JUnit path: /JWave/lib/junit4.jar

init:
[mkdir] Created dir: //JWave/build
[echo] OS Name is: Linux
[echo] OS Arch. is: amd64
[echo] OS Vers. is: 6.1.12-x64v1-xanmod1
[echo] JUnit path: //JWave/lib/junit4.jar
[echo] hamcrest path: //JWave/lib/hamcrest-all.jar

compile:
[javac] Compiling 104 source files to //JWave/build
[javac] error: error reading /JWave/lib/junit4.jar; zip END header not found

BUILD FAILED
//JWave/build.xml:99: Compile failed; see the compiler error output for details.

Total time: 0 seconds

Discrete Wavelet Transform

Hi, this library is super useful. I'm wondering whether you can add Discrete Wavelet Transform to it. Thanks.

Flipped signs in all high pass filter coefficients, error in _buildOrthonormalSpace

Hi!

I found an issue with jwave's wavelet decomposed coefficients having sign differences in detail coefficients when compared with
pywavelet coefficients.

So, I digged through your code, and figured that you are using pywavelets mother wavelet decomposition function as low pass filter coefficients from this page (http://wavelets.pybytes.com/wavelet/db10/) and building the rest using _buildOrthonormalSpace here
(https://github.com/cscheiblich/JWave/blob/master/src/jwave/transforms/wavelets/Wavelet.java#L104)

The high pass filter coefficients you have build using this function do not turn out to be the same as the high pass filter coefficients for the mother wavelet scaling function as given in pywavelets documentation (http://wavelets.pybytes.com/wavelet/db10/)

Here is a demonstration of error in coefficients output from Daubechies10 as compared to python

Python

In [16]: arr = np.zeros(4)

In [17]: arr[1] = 1

In [18]: coeffs = [0] * 3
...: for i in range(3):
...: arr, coeffs[i] = pywt.dwt(arr, "db10", "periodization")
...: print(arr, coeffs[i])
...:
...:
[ 0.30328632 0.40382046] [-0.14391341 0.85102019]
[ 0.5] [ 0.07108838]
[ 0.70710678] [ -5.07923307e-17]

// Java

/**

  • Performs a Forward FastWaveletTransform
  • @param mat
  • @return mat
    */
    public static double[][] forwardDaubechy(double[] mat) {
    Wavelet wavelet = new Daubechies10();
    Transform t = new Transform(new FastWaveletTransform(wavelet));
    double[][] forward = t.decompose(mat);
    return forward;
    }

level 0 [0.3032863181493727, 0.4038204630371676, 0.14391341375270209, -0.8510201949392565]
level 1 [0.49999999999999406, -0.07108837559095565, 0.14391341375270209, -0.8510201949392565]

Error in high pass filter coefficient

In [51]: _scalingDeCom = [-1.3264203002354869e-005, 9.3588670001089845e-005, -0.0001164668549943862, -0.00068585669500468248, 0.0019924052949908499, 0.00139535174699407
...: 98, -0.010733175482979604, 0.0036065535669883944, 0.033212674058933238, -0.029457536821945671, -0.071394147165860775, 0.093057364603806592, 0.12736934033574265
...: , -0.19594627437659665, -0.24984642432648865, 0.28117234366042648, 0.68845903945259213, 0.52720118893091983, 0.18817680007762133, 0.026670057900950818]
...: _motherWavelength = len(_scalingDeCom)
...: _waveletDeCom = [0] * _motherWavelength;
...: for i in range(0, _motherWavelength):
...: if( i % 2 == 0 ):
...: _waveletDeCom[ i ] = _scalingDeCom[ ( _motherWavelength - 1 ) - i ];
...: else:
...: _waveletDeCom[ i ] = -_scalingDeCom[ ( _motherWavelength - 1 ) - i ];
...:

In [52]: _waveletDeCom
Out[52]:
[0.026670057900950818,
-0.18817680007762133,
0.5272011889309198,
-0.6884590394525921,
0.2811723436604265,
0.24984642432648865,
-0.19594627437659665,
-0.12736934033574265,
0.09305736460380659,
0.07139414716586077,
-0.02945753682194567,
-0.03321267405893324,
0.0036065535669883944,
0.010733175482979604,
0.0013953517469940798,
-0.00199240529499085,
-0.0006858566950046825,
0.0001164668549943862,
9.358867000108985e-05,
1.326420300235487e-05]

expected = [-0.026670057900950818
0.18817680007762133
-0.5272011889309198
0.6884590394525921
-0.2811723436604265
-0.24984642432648865
0.19594627437659665
0.12736934033574265
-0.09305736460380659
-0.07139414716586077
0.02945753682194567
0.03321267405893324
-0.0036065535669883944
-0.010733175482979604
-0.0013953517469940798
0.00199240529499085
0.0006858566950046825
-0.0001164668549943862
-9.358867000108985e-05
-1.326420300235487e-05]

Hence the output detail coefficients are differing in signal, since you have used the same flipped high pass filter for building the rest of the coefficients for reconstruction, your inverse transform did not make any difference, but this is an error.

Fix - basically line 112 and 114 signs should be flipped in function _buildOrthonormalSpace, since the first coefficient in high pass filter should have a negative sign -(https://github.com/cscheiblich/JWave/blob/master/src/jwave/transforms/wavelets/Wavelet.java#L104)

protected void _buildOrthonormalSpace( ) {
// building wavelet as orthogonal (orthonormal) space from
// scaling coefficients (low pass filter). Have a look into
// Alfred Haar's wavelet or the Daubechies Wavelet with 2
// vanishing moments for understanding what is done here. ;-)
_waveletDeCom = new double[ _motherWavelength ];
for( int i = 0; i < _motherWavelength; i++ )
if( i % 2 == 0 )
_waveletDeCom[ i ] = -_scalingDeCom[ ( _motherWavelength - 1 ) - i ];
else
_waveletDeCom[ i ] = _scalingDeCom[ ( _motherWavelength - 1 ) - i ];
// Copy to reconstruction filters due to orthogonality (orthonormality)!
_scalingReCon = new double[ _motherWavelength ];
_waveletReCon = new double[ _motherWavelength ];
for( int i = 0; i < _motherWavelength; i++ ) {
_scalingReCon[ i ] = _scalingDeCom[ i ];
_waveletReCon[ i ] = _waveletDeCom[ i ];
} // i
} // _buildOrthonormalSpace

decompose() then recompose() giving incorrect results

From the documentation and the examples, I thought that putting a timeseries through a decompose operation, then putting the resulting matrix through a recompose operation, would give me back the original data (modulo some rounding error potentially).

However, the results I'm getting look completely wrong, even when using a very simple wavelet like Haar1 :-(

Have a look at this test and the output below it:

https://gist.github.com/andrewclegg/9e937b284a91a42f95ae

The result I'm getting back actually looks like the Haar wavelet itself (two constant values). That might be a red herring though...

Any idea what I'm doing wrong?

Symlet

Hi,

I used Haar function before, and I was able to specify the number of steps. Now, I am using symlet, but I am not able to do the same.
May you please provide an example showing how to specify the steps for this function?

Thanks in advance for your help.

Regards,
Mamoun

Maven repo

Hi! Could you add your library to the Central Maven repo, to make it available in the maven projects?

Different with Matlab?

Dear Christian,

I am trying to use this library, and it gives me results. Basically, this is what my code do:

  1. load an image (I use the "woman" image in matlab).
  2. get the pixel values
  3. call forward method (FWT with Haar / Sym8)
    transform.forward(pixels, 1, 1) //apparently, dwt2 in Matlab only apply 1 level FWT)
  4. normalized the result to 0-255
  5. print the image

However, I also tried to use dwt in Matlab and it gives me a different result, most notably are:

  1. For each additional vanishing moment N, the result has more dimension (image size + N - 1)
  2. Image attached for both result in Matlab and Java
    matlabhaar
    normhaar
    matlabsym8
    normsym8

Additionally, I try to compare both library with 4x4 all-ones matrix using Haar Wavelet (again by calling forward(matrix, 1, 1)). The coefficients are basically the same with different signs in upper right and lower left elements.
Here is an example of the result:
-Matlab (I crop it to 4x4 from 18x18)
2 2 -2.97996696763114e-12 -2.97996696763114e-12
2 2 -2.97996696763114e-12 -2.97996696763114e-12
-2.97995268873730e-12 -2.97995268873730e-12 4.44003260755500e-24 4.44003260755500e-24
-2.97995268873730e-12 -2.97995268873730e-12 4.44003260755500e-24 4.44003260755500e-24

-Java
2.0 2.0 2.9799526887373042E-12 2.9799526887373042E-12
2.0 2.0 2.9799526887373042E-12 2.9799526887373042E-12
2.9799669676311424E-12 2.9799669676311424E-12 4.440032607554995E-24 4.440032607554995E-24
2.9799669676311424E-12 2.9799669676311424E-12 4.440032607554995E-24 4.440032607554995E-24

What exactly is happening here? Is there something I do wrong?

question on example.

Hi, I saw an example on the wikipage. I'm wondering why the variable "arrHilb" is 1-dimensional. To my understanding, wavelet coefficients include both scale and translation, so that arrHilb must be 2-dimensional. Am I missing anything here? Thanks.

Transform t = new Transform( new WaveletPacketTransform( new Haar02( ) ) );

double[ ] arrTime = { 1., 1., 1., 1., 1., 1., 1., 1. };

double[ ] arrHilb = t.forward( arrTime ); // 1-D WPT Haar forward

reverse and forward functions

Hello,

I tried to test the forward and reverse functions, but I am not getting the original signal.

Transform t = new Transform( new FastWaveletTransform( new Haar02( ) ),10 );
double[ ] output =t.reverse(t.forward( buffer )) ;

I tried to make the array of length 2^n (32768), but still not getting the original signal.

Am I doing something wrong? or the problem still exists?

I attached the output. Blue is the original signal, red is what I get.

Thanks in advance for your cooperation.

continuous wavelet transforms

Hey, looking through the function signatures, it looks like decompose maybe runs a CWT:

public double[][] decompose(double[] arrTime) { ... }

is this true? If not, do you have any plans on supporting CWTs in the future?

Amazing robust code, wish I understood this scientific domain more so I didn't have to ask such a potentially stupid question.

Consider moving to Maven project layout?

Hi,

Would you consider moving this project to Maven, and publishing the jar on Maven Central? It would be really useful for people who want to use it in other projects.

Moving to the Maven directory layout is really simple:

andrewclegg@8cb33ed

(EDIT: sorry, prematurely submitted by accident)

I think publishing it on Maven Central is pretty straightforward too -- I haven't done so because I'm not the project maintainer, so it might cause confusion. In the pom.xml I wrote for my fork (see above) I just published it in a personal repo instead. That would be another option for you too.

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.