The fast Continuous Wavelet Transform (fCWT) is a highly optimized C++ library for very fast calculation of the CWT in C++, Matlab, and Python.
fCWT has been featured on the January 2022 cover of NATURE Computational Science. In this article, fCWT is compared against eight competitor algorithms, tested on noise resistance and validated on synthetic electroencephalography and in vivo extracellular local field potential data.
Please cite our research paper using the sidebar button when using fCWT in your research project.
Arts, L.P.A., van den Broek, E.L. The fast continuous wavelet transformation (fCWT) for real-time, high-quality, noise-resistant time–frequency analysis. Nat Comput Sci 2, 47–58 (2022). https://doi.org/10.1038/s43588-021-00183-z
New version available:
- fCWT can be seamlessly used in Python (see Jupyter Notebook)
- Interface upgrade: Use frequencies instead of scales and octaves!
- Fixed small memory allignment bugs
- Calculating CWT 34-120x faster than all competitors*
- Very high time-frequency resolution (i.e., it does not rely on wavelet estimation)
- Real-time CWT for signals having sample frequencies of up to 200kHz
- Applicable in many applications ranging from audio and speech to engine vibration analysis
- Easy Python integration via pip
- Easy MATLAB integration via MEX-files
| fCWT for real-time audio and speech analysis | fCWT for high-resolution in-vivo Neuropixel data analysis |
|---|---|
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| fCWT for real-time Electroencephalography (EEG) analysis | fCWT for real-time engine diagnostics |
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*Based on C++ performance. fCWT is the fastest CWT library in C++, Python and Matlab! Please see the benchmark section for more details. Raise an issue if you found a new/faster implementation. I will try to add it to benchmark!
fCWT's implementation can be used to accelerate your C++, Python, and Matlab projects! Build the C++ library to achieve the highest efficiency or use the Matlab and Python packages to maximize integration possibilities.
Install the Python package using pip:
$ pip install fcwt
or if you want to install from source:
$ git clone https://github.com/fastlib/fCWT.git
$ cd fCWT
$ pip install .
See this Jupyter Notebook for documentation.
Build MEX-files from source:
$ git clone https://github.com/fastlib/fCWT.git
$ cd fCWT
$ mkdir -p build
$ cd build
$ cmake ../ -DBUILD_MATLAB=ON
$ make
Two .mex files should now have been created in the MATLAB folder. Run the example.mlx live script to see how to use fCWT in Matlab. fCWT has been tested in R2022b on an Intel Apple Macbook Pro.
Build fCWT from source:
$ git clone https://github.com/fastlib/fCWT.git
$ cd fCWT
$ mkdir -p build
$ cd build
$ cmake ../ [-DBUILD_BENCHMARK=ON|OFF]
$ make
$ sudo make install
See the Installation section for more details about building fCWT from source for both UNIX and Windows systems.
Columns are formatted as X-Y, where X is signal length in samples and Y the number of frequencies. The benchmark has been performed on a MacBook Pro 2019 having a 2,3 GHz Intel Core i9 4.5 Ghz Boost, 16 GB 2400 MHz DDR4. See the 'Usage: Benchmark' section for more details about the C++ benchmark. See the Benchmark Notebook for the fCWT Python benchmark.
| Implementation | 10k-300 | 10k-3000 | 100k-300 | 100k-3000 | Speedup factor |
|---|---|---|---|---|---|
| fCWT (C++) | 0.005s | 0.04s | 0.03s | 0.32s | - |
| fCWT (Python) | 0.011s | 0.089s | 0.074s | 0.66s | - |
| fCWT (Matlab) | 0.072s | 0.44s | 0.17s | 1.55s | - |
| CCWT (Python) | 0.019s | 0.11s | 0.15s | 3.40s | 10.63x |
| PyWavelets (Python) | 0.10s | 1.17s | 1.06s | 12.69s | 34.29x |
| Matlab | 0.75s | 0.86s | 1.06s | 13.26s | 35.85x |
| SsqueezePy (Python) | 0.04s | 0.43s | 1.16s | 17.76s | 48.00x |
| SciPy (Python) | 0.19s | 1.82s | 2.11s | 18.70s | 50.54x |
| Rwave (C) | 0.18s | 1.84s | 2.28s | 23.22s | 62.75x |
| Mathematica | - | - | - | 27.83s | 75.20x |
| Wavelib (C++) | 0.25s | 2.55s | 4.85s | 45.04s | 121.72x |
import fcwt
import numpy as np
import matplotlib.pyplot as plt
#Initialize
fs = 1000
n = fs*100 #100 seconds
ts = np.arange(n)
#Generate linear chirp
signal = np.sin(2*np.pi*((1+(20*ts)/n)*(ts/fs)))
f0 = 1 #lowest frequency
f1 = 101 #highest frequency
fn = 200 #number of frequencies
#Calculate CWT without plotting...
freqs, out = fcwt.cwt(signal, fs, f0, f1, fn)
#... or calculate and plot CWT
fcwt.plot(signal, fs, f0=f0, f1=f1, fn=fn)Output:
#include <iostream>
#include <vector>
#include <math.h>
#include <fcwt.h>
int main(int argc, char * argv[]) {
int n = 100000; //signal length
const int fs = 1000; //sampling frequency
float twopi = 2.0*3.1415;
//3000 frequencies spread logartihmically between 1 and 32 Hz
const float f0 = 1;
const float f1 = 32;
const int fn = 3000;
//Define number of threads for multithreaded use
const int nthreads = 8;
//input: n real numbers
std::vector<float> sig(n);
//output: n x scales
std::vector<complex<float>> tfm(n*fn);
//initialize with 1 Hz cosine wave
for(auto& el : sig) {
el = cos(twopi*((float)(&el - &sig[0])/(float)fs));
}
//Create a wavelet object
Wavelet *wavelet;
//Initialize a Morlet wavelet having sigma=2.0;
Morlet morl(2.0f);
wavelet = &morl;
//Create the continuous wavelet transform object
//constructor(wavelet, nthreads, optplan)
//
//Arguments
//wavelet - pointer to wavelet object
//nthreads - number of threads to use
//optplan - use FFTW optimization plans if true
//normalization - take extra time to normalize time-frequency matrix
FCWT fcwt(wavelet, nthreads, true, false);
//Generate frequencies
//constructor(wavelet, dist, fs, f0, f1, fn)
//
//Arguments
//wavelet - pointer to wavelet object
//dist - FCWT_LOGSCALES | FCWT_LINFREQS for logarithmic or linear distribution frequency range
//fs - sample frequency
//f0 - beginning of frequency range
//f1 - end of frequency range
//fn - number of wavelets to generate across frequency range
Scales scs(wavelet, FCWT_LINFREQS, fs, f0, f1, fn);
//Perform a CWT
//cwt(input, length, output, scales)
//
//Arguments:
//input - floating pointer to input array
//length - integer signal length
//output - floating pointer to output array
//scales - pointer to scales object
fcwt.cwt(&sig[0], n, &tfm[0], &scs);
return 0;
}- Cmake >=3.10
- C++17 compiler (GCC 10+, Clang or Microsoft Visual C++ 15.7+);
- FFTW >=3.3 (if you choose to use own FFTW installation)
- OpenMP >=5
fCWT has been tested on Mac OSX Mojave 10.14.5, Big Sur 11.6 (both on Intel and Apple Silicon), Windows 10, and Ubutnu 20.04. Please raise an issue if you experience issues running fCWT on these systems! We are working very hard on getting fCWT to run on as many platforms as possible. The benchmark has been performed on a MacBook Pro 2019 having a 2,3 GHz Intel Core i9, 16 GB 2400 MHz DDR4.
Settings that may be specified at build time by using CMake variables are:
- the flag to build a shared library instead of static (default is on);
- whether or not you want to use your own FFTW installation*;
- whether or not you want to build the
BENCHMARKtarget; - whether or not you want to build the
MEXfiles for MATLAB; - installation directories.
Details:
| CMake variable | Possible values | Default on Unix | Default on Windows |
|---|---|---|---|
| The flag to build the shared library | |||
| BUILD_SHARED_LIBS | On | Off | On | On |
| The flag to use own FFTW installation (e.g., via brew or apt-get) | |||
| USE_OWN_FFTW * | On | Off | Off | Off |
| The flag to build benchmark target | |||
| BUILD_BENCHMARK | On | Off | Off | Off |
| The flag to build MATLAB MEX files | |||
| BUILD_MATLAB | On | Off | Off | Off |
| Installation directories | |||
| FCWT_MATLAB_DIR | a path relative to build |
"../MATLAB" | "../MATLAB" |
| CMAKE_INSTALL_PREFIX | an absolute path | "/usr/local" | "%ProgramFiles (x86)%\fCWT" |
| FCWT_CMAKE_INSTALL_DIR | a path relative to CMAKE_INSTALL_PREFIX | "share/fcwt/cmake" | "cmake" |
| FCWT_LIB_INSTALL_DIR | a path relative to CMAKE_INSTALL_PREFIX | "lib" | "lib" |
| FCWT_INCLUDE_INSTALL_DIR | a path relative to CMAKE_INSTALL_PREFIX | "include" | "include" |
- Please note that you'll need to configure FFTW to use OpenMP for multithreading and 256-bit vector instructions (e.g., AVX) to obtain comparable results to the benchmark. Standard configuration binaries obtained via
breworapt-getgenerally don't have AVX enabled.
$ git clone https://github.com/fastlib/fCWT.git
$ cd fCWT
$ mkdir -p build
$ cd build
$ cmake ../
$ make
$ sudo make install
Run the Developer Command Prompt for Visual Studio and type:
> git clone https://github.com/fastlib/fCWT.git
> cd fCWT
> mkdir -p build
> cd build
> cmake -G "Visual Studio 17 2022" ..
> cmake --build .
A Visual Studio .SLN file has now been created in the build-folder. This project includes several build targets. To build a shared/static library of fCWT, build the 'fCWT' target. To run the example code, set the 'fCWT_example' target as the start-up project and run the code as 'release'.
To install fCWT, run the Elevated Command Prompt (i.e. the command prompt with administrator privileges) in the build-folder and type:
> cmake -P cmake_install.cmake
To make the installed DLL available for any application that depends on it, the symbolic link to the fcwt.dll should be created:
- in %SYSTEMROOT%\System32 for the 64-bit DLL on 64-bit host (or for 32-bit DLL on 32-bit host);
- in %SYSTEMROOT%\SysWOW64 for the 32-bit DLL on 64-bit host.
If you want to replicate the benchmark as presented in the paper, you can build the benchmark target from source using the BUILD_BENCHMARK flag. Additionally, you need Rafat's Wavelib library (https://github.com/rafat/wavelib). Follow its instructions to build a static library file and install the library on your system's header and library paths (/usr/local/include and /usr/local/lib for UNIX systems).
There are several commands to use the benchmark build:
$ ./fCWT_benchmark -h
Get information about fCWT usage
$ ./fCWT_benchmark -fcwtoptimize [Length=100000] [Number of threads=8] [Optimization=estimate|measure|patient|exhaustive]
Calculate all optimization schemes for all powers of two up to the maximal power of two that is calculated based on the [Length] argument, using [Number of threads] threads and using the optimization method specified. If no optimization method is defined it will use the measure method. Estimate is the fastest but lowest quality optimization, exhaustive is the slowest but highest quality optimization. Warning: Exhaustive will take some time!
$ ./fCWT_benchmark -fcwt [Length=100000] [Number of threads=8]
Calculate the CWT using fCWT for three signals with length=[Length]. The signals are generated by a dynamic sine wave function, random function and non-smooth function generator, respectively. Depending on optimization usage and system hardware, a run-time in the order of seconds is expected for lengths up to 100.000. Furthermore, one will see that signal content is independent of performance. See the source code for more information.
$ ./fCWT_benchmark -wavelib [Length=100000] [Number of threads=8]
Calculate the CWT using Wavelib`s C implementation [1] for a signal with length=[Length]. Depending on system hardware, a run-time in the order of several minutes is expected for lengths up to 100.000.
$ ./fCWT_benchmark -rwave [Length=100000] [Number of threads=8]
Calculate the CWT using Rwave's C implementation [2] for a signal with length=[Length]. Depending on system hardware, a run-time in the order of several minutes is expected for lengths up to 100.000.
If one wants to reproduce the benchmark results as stated in the article, one has to use a signal length of 100000, 8 threads and calculate optimization plans with the exhaustive method: $ ./fCWT_example -fcwtoptimize 100000 8 exhaustive After that:
- Run fCWT with:
$ ./fCWT_example -fcwt 100000 8, - Run RWave with:
$ ./fCWT_example -rwave 100000 8, - Run Wavelib with:
$ ./fCWT_example -wavelib 100000 8, - Run additional Python, Matlab and Mathematica scripts found in
/src/benchmark
By default, the source code performs 10 runs for demonstration purposes. To match the number of runs in the paper, adjust the runs variable in benchmark.cpp:132. It is recommended to close any background processes.
In the MATLAB-folder, we provided an example live-script titled example.mlx. The example includes basic MATLAB-implementation on how to generate fCWT optimization plans and calculate time-frequency matrices using fCWT. To use fCWT with MATLAB, make sure you generate the MEX-files using the commands listed in the quickstart section.
Note: Expect a decrease in performance when using fCWT via MATLAB. The official benchmark tests MATLAB's CWT implementation via the MATLAB interface and fCWT via the command line. We advice to use fCWT's MATLAB interface solely for plotting purposes.
fCWT is distributed under Apache 2.0 license. For conditions of distribution and use, see file LICENSE.TXT
[1] Rafat. Wavelib. https://github.com/rafat/wavelib. Accessed: 2020-04-22.
[2] R. Carmona, W.-L. Hwang, and B. Torresani.Practical Time-Frequency Analysis: Gabor and wavelettransforms, with an implementation in S. Academic Press, 1998. [p1, 7, 10]