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https://github.com/panda-official/waveletbuffer
A universal C++ compression library based on wavelet transformation
https://github.com/panda-official/waveletbuffer
compression cpp cpp20 signal-processing wavelet wavelet-transform
Last synced: about 2 months ago
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A universal C++ compression library based on wavelet transformation
- Host: GitHub
- URL: https://github.com/panda-official/waveletbuffer
- Owner: panda-official
- License: mpl-2.0
- Created: 2022-03-30T10:28:00.000Z (over 2 years ago)
- Default Branch: develop
- Last Pushed: 2023-07-21T08:57:28.000Z (over 1 year ago)
- Last Synced: 2024-04-14T08:03:12.019Z (8 months ago)
- Topics: compression, cpp, cpp20, signal-processing, wavelet, wavelet-transform
- Language: C++
- Homepage: https://waveletbuffer.readthedocs.io/en/latest/
- Size: 586 KB
- Stars: 12
- Watchers: 3
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
- Authors: AUTHORS.md
Awesome Lists containing this project
README
[](https://codecov.io/gh/panda-official/WaveletBuffer)
# WaveletBuffer
A universal C++ compression library based on wavelet transformation
## Features
- Written in Modern C++
- One-side wavelet decomposition for vectors and matrices
- 5 Daubechies Wavelets DB1-DB5
- Different denoising algorithms
- Fast and efficient compression with [MatrixCompressor](https://github.com/panda-official/MatrixCompressor)
- Cross-platform
## Requirements
* CMake >= 3.16
* C++20 compiler
* conan >= 1.56, < 2.0
## Bindings
* [Python](python/README.md)
## Usage Example
```c++
#include
using drift::Signal1D;
using drift::WaveletBuffer;
using drift::WaveletParameters;
using drift::WaveletTypes;
using DenoiseAlgo = drift::ThresholdAbsDenoiseAlgorithm;
int main() {
Signal1D original = blaze::generate(
1000, [](auto index) { return static_cast(index % 100); });
std::cout << "Original size: " << original.size() * 4 << std::endl;
WaveletBuffer buffer(WaveletParameters{
.signal_shape = {original.size()},
.signal_number = 1,
.decomposition_steps = 3,
.wavelet_type = WaveletTypes::kDB1,
});
// Wavelet decomposition of the signal and denoising
buffer.Decompose(original, DenoiseAlgo(0, 0.3));
// Compress the buffer
std::string arch;
buffer.Serialize(&arch, 16);
std::cout << "Compressed size: " << arch.size() << std::endl;
// Decompress the buffer
auto restored_buffer = WaveletBuffer::Parse(arch);
Signal1D output_signal;
// Restore the signal from wavelet decomposition
restored_buffer->Compose(&output_signal);
std::cout << "Distance between original and restored signal: "
<< blaze::norm(original - output_signal) / original.size()
<< std::endl;
std::cout << "Compression rate: " << original.size() * 4. / arch.size() * 100
<< "%" << std::endl;
}
```
## Build and Installing
On Ubuntu:
```
git clone https://github.com/panda-official/WaveletBuffer.git
mkdir build && cd build
cmake -DWB_BUILD_TESTS=ON -DWB_BUILD_BENCHMARKS=ON -DWB_BUILD_EXAMPLES=ON -DCODE_COVERAGE=ON ..
cmake --build . --target install
```
On MacOS:
```
git clone https://github.com/panda-official/WaveletBuffer.git
mkdir build && cd build
cmake -DWB_BUILD_TESTS=ON -DWB_BUILD_BENCHMARKS=ON -DWB_BUILD_EXAMPLES=ON -DCODE_COVERAGE=ON ..
cmake --build . --target install
```
On Windows:
```
git clone https://github.com/panda-official/WaveletBuffer.git
mkdir build && cd build
cmake -DWB_BUILD_TESTS=ON -DWB_BUILD_BENCHMARKS=ON -DWB_BUILD_EXAMPLES=ON -DCODE_COVERAGE=ON ..
cmake --build . --config Release --target install
```
## Integration
### Using cmake target
```cmake
find_package(wavelet_buffer REQUIRED)
add_executable(program program.cpp)
target_link_libraries(program wavelet_buffer::wavelet_buffer)
# WaveletBuffer use blaze as linear algebra library which expects you to have a LAPACK library installed
# (it will still work without LAPACK and will not be reduced in functionality, but performance may be limited)
find_package(LAPACK REQUIRED)
target_link_libraries(program ${LAPACK_LIBRARIES})
```
## References
* [Documentation](https://waveletbuffer.readthedocs.io)
* [Drift Protocol](https://github.com/panda-official/DriftProtocol) - Protobuf Libraries to encode message in Drift infrastructure
* [Drift Python Client](https://github.com/panda-official/DriftPythonClient) - Python Client to access data of _PANDA|Drift_