Saravanan666/pypi-org huggingface dataset:
Package: wavecracker
Wave CrackerAbstractPython batch tool for signal time/frequency analysis (input data: CSV or audio files).Detailed documentation:herePlot examplesSetupYou can install the package using pip:python-mpipinstallwavecrackerPython DependenciesModuleVersion requiredNotesPython>= 3.7Preferably > 3.8 and < 3.12, ref.documentationfor additional detailsnumpy>= 1.23.5PyYAML>= 6.0pandas>= 2.0.1chardet>= 4.0.0scipy>= 1.10.1matplotlib>= 3.7.1PyWavelets>= 1.4.1Optional (for wavelet transform)pydub>= 0.25.1Optional (for audio file processing)moviepy>= 1.0.3Optional (for audio file processing)psutil>= 5.9.5Optional (for hardware detailed diagnostics upon boot)Installing extras (i.e. optional Python dependencies)The following commands allow the installation of the extras correspondent to the optional dependencies above mentioned:python-mpipinstallwavecracker[wavelet]python-mpipinstallwavecracker[audio]python-mpipinstallwavecracker[hwdiagnostics]Note that some of these extras may or may not work depending on the Python version. More information in thedocumentation.Other DependenciesAs mentioned above, the following directories are needed in thePATH:PYTHON_HOMEandPYTHON_HOME/ScriptsAlso, but only if you are willing to process audio files, these directories must be present in thePATH, too.The directory containing the FFMPEG executable (6.0 or upper; note, needed only for audio files processing)UsageWe assume, in the following, that${PACKAGE_HOME}is where the package is available after the install (typically under${PYTHON_HOME}/Lib/site-packages/wavecrackeror alike).Open a command shell, make sure your Python install is in thePATH, and enter the following command:python"${PACKAGE_HOME}/signalanalyzer.py"<arguments>Many other details can be found in thedocumentationabout:Available command line argumentsParameters in the configuration fileLaunching the DEMOpython"${PACKAGE_HOME}/demo/launch_demo.py"ExamplesExample 1 (CSV file processing)...../signalanalyzer.py--input-path./inputdata.csv--qplottime_1signal_1--include-histogram--out-directory./output_1Explanation:This would process a csv file namedinputdata.csv(in the current directory), looking for a header with columns
namedtime_1andsignal_1, saving the results into a subdirectory of the current directory namedoutput_1.Plots typically generated are Fourier transform and othersA CSV is saved (with data about some of the calculated functions).Note: the output directory needs to be existing.A directory namedlogshas to be created in the current directory for the logs to be saved.
Package: wavedisp
WavedispIntroductionWavedisp is a package to help you in displaying waveforms for different HDL simulators or VCD viewers in a portable
way. It generates waveform files for GTKWave, Modelsim and RivierPro by genereting tcl scripts loaded by the HDL viewer
from a unique representation described in python.LicenseWavedisp is distributed under the GPLv3, the complete license description can be foundhere.General informationAn example of use is availablehere. Of course, contribution are welcomed.
Package: wavedrom
WaveDromPyThis is a python module and command line fully compatible withWaveDrom, which is originally implemented in JavaScript. It is useful if you want to generate wavedrom diagrams from a python environment or simply don't want to install theNode.jsenvironment just to use WaveDrom as simple command line.WaveDromPy is for example used insphinxcontrib-wavedromto render wavedrom for Sphinx documentation. While the original project renders the diagrams in JavaScript in the browser, WaveDromPy renders them to SVG files.This tool is a direct translation of original Javascript fileWaveDrom.jsto Python. No extra feature added. We seek to have it fully compatible.The toolWaveDromPydirectly convertsWaveDromcompatible JSON files into SVG format.InstallationIt is most easy to just install wavedrom via pip/pypi:pip install wavedromAlternatively you can install the latest version from this repository:pip install git+https://github.com/wallento/wavedrompyor from your local copy:pip install .UsageYou can either use the tool from Python:import wavedrom
svg = wavedrom.render("""
{ "signal": [
 { "name": "CK",   "wave": "P.......",                                              "period": 2  },
 { "name": "CMD",  "wave": "x.3x=x4x=x=x=x=x", "data": "RAS NOP CAS NOP NOP NOP NOP", "phase": 0.5 },
 { "name": "ADDR", "wave": "x.=x..=x........", "data": "ROW COL",                     "phase": 0.5 },
 { "name": "DQS",  "wave": "z.......0.1010z." },
 { "name": "DQ",   "wave": "z.........5555z.", "data": "D0 D1 D2 D3" }
]}""")
svg.saveas("demo1.svg")This will render a waveform as:You can find more examplesin the WaveDrom tutorial.A second feature is that WaveDrom can render logic circuit diagrams:import wavedrom
svg = wavedrom.render("""
{ "assign":[
  ["out",
    ["|",
      ["&", ["~", "a"], "b"],
      ["&", ["~", "b"], "a"]
    ]
  ]
]}""")
svg.saveas("demo2.svg")This will render a as:You can find more examplesin the WaveDrom tutorial2.Finally, wavedrom can draw registers as bitfields:import wavedrom
svg = wavedrom.render("""
{"reg": [
  { "name": "IPO",   "bits": 8, "attr": "RO" },
  {                  "bits": 7 },
  { "name": "<o>B</o><b>R<i>K</i></b>",   "bits": 5, "attr": "RW", "type": 4 },
  { "name": "CPK",   "bits": 1 },
  { "name": "Clear", "bits": 3 },
  { "bits": 8 }
  ]
]}""")
svg.saveas("demo3.svg")This will render as:This mode is documented as part of thebit-fieldJavaScript package.Alternatively, WaveDromPy can be called from the command line:wavedrompy --input input.json --svg output.svgImportant noticeThe command line uses Python's JSON interpreter that is more restrictive (coherent with the JSOC spec), while the JavaScript json is more relaxed:All strings have to be written between quotes (""),Extra comma (,) not supported at end of lists or dictionariesAsciiDoctor exampleAnAsciiDoctorexample is provided to directly generate timing diagrams fromAsciiDoctorformatted documents.
Package: wavedrom-ascii
Wavedrom ASCIIAn ASCII (actually Unicode) representation ofWavedromwaves and bitfields.ComparisonExample Wavedrom JSON:{"signal":[{"name":"clk","wave":"p......"},{"name":"bus","wave":"x.34.5x","data":"head body tail"},{"name":"wire","wave":"0.1..0."}]}Wavedrom image:ASCII representation:┌────┐    ┌────┐    ┌────┐    ┌────┐    ┌────┐    ┌────┐    ┌────┐    
  clk   ┘    └────┘    └────┘    └────┘    └────┘    └────┘    └────┘    └────
        ────────────────────╥─────────╥───────────────────╥─────────╥─────────
  bus   XXXXXXXXXXXXXXXXXXXX║ head    ║ body              ║ tail    ║XXXXXXXXX
        ────────────────────╨─────────╨───────────────────╨─────────╨─────────
                            ┌─────────────────────────────┐                   
  wire  ────────────────────┘                             └───────────────────The Bitfield example from Wavedrom:Will be rendered as:31  2928  262524      2019      1514  1211       7 6           0
┌─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┐
│ nf  │ mop │v│  lumop  │   rs1   │width│   vd    │0 0 0 0 1 1 1│
└─┴─┴─┴─┴─┴─┴─┴─┴─┴─┴─┴─┴─┴─┴─┴─┴─┴─┴─┴─┴─┴─┴─┴─┴─┴─┴─┴─┴─┴─┴─┴─┘
Package: wavefile
Pythonic libsndfile wrapper to read and write audio files.FeaturesWriter and reader objects are context managersFormat, channels, length, sample rate... are accessed as properties as well as text stringsReal multichannel (not just mono/stereo)All libsndfile formats supported, floating point encodings by defaultNumpy based interfaceGenerators for block by block readingReading reuses the same data block to avoid many data allocationsShortened constant names for formats (Using scopes instead of prefixes)Matlab-like whole-file interface (not recommended in production code but quite convenient for quick scripting)Transparent UTF-8 handling for filenames and text stringsNo module compilation required (wraps the dll using ctypes)Works both for Python3 >= 3.8You can find the latest version at:https://github.com/vokimon/python-wavefile
Package: waveforms
waveformsForm waveforms used in experiment.InstallationWe encourage installing waveforms via the pip tool (a python package manager):python-mpipinstallwaveformsTo install from the latest source, you need to clone the GitHub repository on your machine.gitclonehttps://github.com/feihoo87/waveforms.gitThen dependencies andwaveformscan be installed in this way:cdwaveforms
python-mpipinstallnumpy
python-mpipinstall-e.Usageimportnumpyasnpimportmatplotlib.pyplotaspltfromwaveformsimport*pulse=cosPulse(20e-9)x_wav=zero()y_wav=zero()I,Q=mixing(0.5*pulse,freq=-20e6,DRAGScaling=0.2)x_wav+=Iy_wav+=QI,Q=mixing(pulse>>1e-6,freq=-20e6,phase=np.pi/2,DRAGScaling=0.2)x_wav+=Iy_wav+=QI,Q=mixing((0.5*pulse)>>2e-6,freq=-20e6,DRAGScaling=0.2)x_wav+=Iy_wav+=Qt=np.linspace(-1e-6,9e-6,10001)plt.plot(t,x_wav(t))plt.plot(t,y_wav(t))plt.show()Reporting IssuesPlease report all issueson github.LicenseMIT
Package: wavefront-api-client
&lt;p&gt;The REST API enables you to interact with the Wavefront service by using standard REST API tools. You can use the REST API to automate commonly executed operations, for example to tag sources automatically.&lt;/p&gt;&lt;p&gt;When you make REST API calls outside the REST API documentation UI, to authenticate to the service, you must use an API token associated with a user account or a service account. For information on how to get the API token and examples, see &lt;a href&#x3D;&quot;http://docs.wavefront.com/using_wavefront_api.html&quot;&gt;Use the Wavefront REST API.&lt;/a&gt;&lt;/p&gt;  # noqa: E501
Package: wavefront_reader
wavefront_readerA parser for wavefront .obj and .mtl filesFree software: MIT licenseDocumentation:https://wavefront-reader.readthedocs.io.FeaturesReads out wavefront objects to dictionaries with their attributes, including their materials:from wavefront_reader import read_wavefront, read_objfile, read_mtlfile
geoms = read_wavefront('myObjects.obj')
cube = geoms['Cube']
cube_vertices = cube['v']
cube_diffuse_material = cube['material']['Kd']The module has a lot of tests, and handles face indexing by re-indexing the vertex, normal, and texcoord arrays
simply by reindexing them into same-length, sequential arrays.  While this reduces the memory benefits of the .obj
format, it makes it much easier to load the data into OpenGL or reindex the data yourself.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2017-01-18)First release on PyPI.
Package: waveglow
Waveglow libraryThis Waveglow library was changed to be used with vait library.Instalation1) Install waveglow library(This will also install tacotron2 library)pipinstallwaveglow==22.12.282) Install CUDA 11.3 or 11.6pipinstall-rrequirements-cuda-11.3.txt# orpipinstall-rrequirements-cuda-11.6.txt3) Install apexgitclonehttps://github.com/NVIDIA/apex/home/${USER}/apexcd/home/${USER}/apex
pipinstall-v--disable-pip-version-check--no-cache-dir./cd-4) Download published model fileswgethttps://drive.google.com/open?id=1rpK8CzAAirq9sWZhe9nlfvxMF1dRgFbF5) Download mel-spectrogramswgethttps://drive.google.com/file/d/1g_VXK2lpP9J25dQFhQwx7doWl_p20fXA/view?usp=sharingUsage: Creating audiowaveglow-inference-f<(lsmel_spectrograms/*.pt)-wwaveglow_256channels_universal_v5.pt-o.--is_fp16-s0.6Usage: Trainingmkdircheckpoints
waveglow-train-cconfig.json
Package: waveglow-cli
waveglow-cliCommand-line interface (CLI) to train WaveGlow using .wav files.Featurestrain/synthesize on CPU or GPUdownload pre-trained models by NvidiaInstallationpipinstallwaveglow-cli--userUsageusage: waveglow-cli [-h] [-v] {download,train,continue-train,validate,synthesize,synthesize-wav} ...

This program trains WaveGlow.

positional arguments:
  {download,train,continue-train,validate,synthesize,synthesize-wav}
                              description
    download                  download pre-trained checkpoint from Nvidia
    train                     start training
    continue-train            continue training
    validate                  validate checkpoint(s)
    synthesize                synthesize mel-spectrograms into an audio signal
    synthesize-wav            synthesize audio file into an audio signal

options:
  -h, --help                  show this help message and exit
  -v, --version               show program's version number and exitPretrained ModelsLJS-v3-580000: Adapted model trained on LJ Speech dataset byNvidia.Audio Example"The North Wind and the Sun were disputing which was the stronger, when a traveler came along wrapped in a warm cloak."Listen here(headphones recommended)RoadmapOutsource method to convert audio files to mel-spectrograms before trainingImprove loggingAdd more audio examplesAdding testsDevelopment setup# updatesudoaptupdate# install Python 3.8-3.11 for ensuring that tests can be runsudoaptinstallpython3-pip\python3.8python3.8-devpython3.8-distutilspython3.8-venv\python3.9python3.9-devpython3.9-distutilspython3.9-venv\python3.10python3.10-devpython3.10-distutilspython3.10-venv\python3.11python3.11-devpython3.11-distutilspython3.11-venv# install pipenv for creation of virtual environmentspython3.8-mpipinstallpipenv--user# check out repogitclonehttps://github.com/stefantaubert/waveglow.gitcdwaveglow# create virtual environmentpython3.8-mpipenvinstall--devRunning the tests# first install the tool like in "Development setup"# then, navigate into the directory of the repo (if not already done)cdwaveglow# activate environmentpython3.8-mpipenvshell# run teststoxFinal lines of test result output:py38: commands succeeded
py39: commands succeeded
py310: commands succeeded
py311: commands succeeded
congratulations :)LicenseMIT LicenseAcknowledgmentsModel code adapted fromNvidia.Paper:Waveglow: A Flow-based Generative Network for Speech SynthesisFunded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 416228727 – CRC 1410CitationIf you want to cite this repo, you can use the BibTeX-entry generated by GitHub (seeAbout => Cite this repository).Taubert, S. (2024). waveglow-cli (Version 0.0.2) [Computer software]. [https://doi.org/10.5281/zenodo.10569141](https://doi.org/10.5281/zenodo.10569141)
Package: waveglow-vocoder
WaveGlow VocoderA vocoder that can convert audio to Mel-Spectrogram and reverse withWaveGlow, all on GPU.Most code is fromTacotron2andWaveGlow.Installpip install waveglow-vocoderExampleOriginalVocodedoriginal musicvocoded musicoriginal speechvocoded speechUsageLoad wav file as torch tensor on GPU.importlibrosay,sr=librosa.load(librosa.util.example_audio_file(),sr=22050,mono=True,duration=10,offset=30)y_tensor=torch.from_numpy(y).to(device='cuda',dtype=torch.float32)Apply mel transform, this would be done on GPU(if ava).fromwaveglow_vocoderimportWaveGlowVocoderWV=WaveGlowVocoder()mel=WV.wav2mel(y_tensor)Decoder it with Waveglow.NOTE:As the hyperparameter of pre-trained model is alignment withTacotron2, one might get totally noise if the Mel spectrogram comes from other function thanwav2mel(an alias for TacotronSTFT.mel_spectrogram).Support for the melspectrogram from librosa and torchaudio is under development.wav=WV.mel2wav(mel)Other pretrained model / Train with your own dataThis vocoder will download pre-trained model frompytorch hubon the first time of initialize.You can also download the latest model fromWaveGlow, or  with your own data and pass the path to the waveglow vocoder.config_path="your_config_of_model_training.json"waveglow_path="your_model_path.pt"WV=WaveGlowVocoder(waveglow_path=waveglow_path,config_path=config_path)Then use it as usual.TODOWaveRNN VocoderMelGAN VocoderPerformanceSupport librosa Mel inputReferenceWaveGlowTacotron2Wavenet Vocoder
Package: waveguide
WaveguideCalculate the various properties of rectangular waveguidesFor example:Cutoff frequencyPhase constantAttenuation constant due to conductor and/or dielectric lossExample 1: Properties of a WR-90 WaveguideWR-90 waveguide:importnumpyasnpimportscipy.constantsasscimportmatplotlib.pyplotaspltfromwaveguideimportphase_constant,attenuation_constant# WR-90 waveguide dimensionsa,b=0.9*sc.inch,0.45*sc.inch# Conductivity of waveguide walls, S/mcond=2e7# Frequency sweepfreq=np.linspace(7,13,100)*sc.gigaPhase constant:beta=phase_constant(freq,a,b,cond=cond)plt.figure()plt.plot(freq/1e9,beta)plt.ylabel(r"Phase constant, $\beta$ (rad/m)")plt.xlabel("Frequency (GHz)")plt.xlim([7,13])Attenuation constant:alpha=attenuation_constant(freq,a,b,cond=cond)plt.figure()plt.plot(freq/1e9,alpha)plt.ylabel(r"Attenuation constant, $\alpha$ (Np/m)")plt.xlabel("Frequency (GHz)")plt.xlim([7,13])Example 2: Cutoff Frequenciesimportnumpyasnpimportscipy.constantsasscfromwaveguideimportcutoff_frequency# Waveguide sizes to analyze (EIA designations)wr_sizes=np.array([28,22.4,18.8,14.8,12.2,10,6.5,5.1,4.3,3.4,2.8])# Calculate cutoff frequenciesf_center=np.empty_like(wr_sizes)f1=np.empty_like(wr_sizes)f2=np.empty_like(wr_sizes)fori,_wrinnp.ndenumerate(wr_sizes):a=_wr*10*sc.mil# waveguide widthf1[i]=cutoff_frequency(a,a/2,m=1,n=0)*1.25# TE10f2[i]=cutoff_frequency(a,a/2,m=2,n=0)*0.95# TE20f_center[i]=(f1[i]+f2[i])/2# Plotfig,ax=plt.subplots(figsize=(12,12))ax.loglog(wr_sizes,f_center/1e9,'ko')ax.errorbar(wr_sizes,f_center/1e9,yerr=[(f_center-f1)/1e9,-(f_center-f2)/1e9],c='k',fmt='o',ls='--',capsize=5,capthick=2)ax.set_xlabel("\nEIA waveguide designation (\"WR-\")")ax.set_ylabel("Frequency range (GHz)")ax.set_ylim([20,500])ax.grid(which='both')plt.yticks(ticks=[20,30,40,50,60,70,80,90,100,200,300,400,500],labels=[20,30,40,50,60,70,80,90,100,200,300,400,500])ax.set_xticks(ticks=wr_sizes,minor=False)ax.set_xticks(ticks=[],minor=True)plt.xticks(ticks=wr_sizes,labels=wr_sizes,rotation=90)Example 3: Alumina-Filled WR-28 WaveguideWR-28 waveguide filled with 1 inch long alumina slug:importnumpyasnpimportscipy.constantsasscimportmatplotlib.pyplotaspltimportwaveguideaswg# WR-28 waveguide dimensionsa,b=0.28*sc.inch,0.14*sc.inch# Conductivity of waveguide walls, S/mcond=1.8e7# Frequency sweepfreq=np.linspace(22,42,401)*sc.giga# Relativity permittivityer=9.3# Alumina length, mlength=1*sc.inch# Section lengthstotal_length=1.7*sc.inchlength1=(total_length-length)/2length2=lengthlength3=length1# S-parameters_,_,s21,_=wg.dielectric_sparam(freq,a,b,er,0,cond,length1,length2,length3)fig,ax=plt.subplots()ax.plot(freq/1e9,20*np.log10(np.abs(s21)))plt.ylabel(r"$S_{21}$ magnitude (dB)")plt.xlabel("Frequency (GHz)")plt.xlim([22,42])
Package: wave-length
# 依赖库numpypandasmatpltlib# 介绍该库使用步长为0.1 nm 的标准CIE色度匹配图进行光谱波长计算。# 使用教程work(file, start=380, end=780, draw=True, statr_line=8)file文件须为 txt 文档，格式如下：[nm]   ;[counts] ;[counts] ;[counts]143.968;  270.000;    0.000;    0.000144.581;  295.000;    0.000;    0.000145.194;  261.000;    0.000;    0.000145.807;  292.429;    0.000;    0.000# 参数使用规则file：你的txt数据存储文件start：计算波谱的起点——默认为 390nm。自定义值必须在区间 [390, 830]中，默认为390end： 计算波谱的终点——默认为830nm。 自定义值必须在区间 [390, 830]中， 默认为830draw：是否绘制展示图，默认为Truestart_line：读取txt文档数据的起点，默认为8。如果你的txt文档有效数据是从第1行开始的，则将该参数设置为0。# 返回的参数
x： CIE xy色度图的x值y： CIE xy色度图的y值result_lambda：你的波谱计算得到的波长Lambda：CIE1931色彩空间色度图波长，步长为0.1nm —— array。 范围为 390——830Lambda_x：CIE1931色彩空间色度图各波长对应的x —— arrayLambda_y：CIE1931色彩空间色度图各波长对应的y —— array# 运行文件运行需要两个关键文件，需要在下面网址去下载；链接：https://pan.baidu.com/s/1CNjeEJy5hTK4IbnActwj2w提取码：cg2q将里面的两个文件放在你自己代码的同级目录下。然后运行如下代码即可from wavelength import wavelengthwavelength.work(“file_path”)
Package: waveletai
WaveletAIA Machine Learning Lifecycle Platform for AI/ML individuals and teams.小波AI是一个集合数据、编程、模型、算力、服务的一站式协同开发云平台，提供全栈的AI能力。在这里，你可以根据业务需要，轻松的来训练、部署、管理和跟踪你的模型。小波AI已在服务于微企多个团队，稳定运行了一年，大大加速了AI项目的实施和推进速度，目前在平台已经完成了上百种模型的前期验证、训练和发布，并在AI市场中发布了多种能力。Power ByWaveletPlus功能特性全生命周期跟踪小波AI会自动关联跟踪记录模型运行时信息，记录训练过程状态和运行数据，支持自定义参数、指标设置并生成实时日志，帮助你快速进行试验，验证想法；小波AI支持完全支持用于跟踪工作的 Git 存储库(支持HTTP/SSH双协议)，在向小波AI提交模型训练时，系统会将有关存储库的信息作为训练过程的一部分进行跟踪；丰富的AI套件支持小波AI平台可用于任何类型的机器学习，从传统 ml 到深度学习、监督式和非监督式学习。无论你是否希望编写 Python 或 R 代码，你都可以在小波AI中构建、训练和跟踪你的模型。该平台还可与常用的AI套件快速集成（如 PyTorch、TensorFlow 、keras、xgboost、 scikit-learn、lightgbm、spark、onnx等）。为大型项目协作而生小波AI适用于大型项目开发场景，让你从大量的离线作业中脱身出来，不同角色得人都可以在平台中实现快速协作；用DevOps的方式管理你的ML工作，从开发到部署的自动流程化实现。大大降低模型迭代开发及部署实施的难度；提供强有力的设备算力支持和资源分配策略，让你可以在CPU、GPU设备上快速训练；通用权限设计，满足组内成果共享和组间权限控制等实际场景，让多项目并行管理不再困难；无需调整业务应用接口，支持模型版本的在线更新；在任何地方以相同方式运行小波AI 会将项目模型打包为再任何平台上可重现的模式；一键发布模型，快速实现模型的云端和终端设备部署；团队@Author  : WaveletAI-Product-Team
Package: wavelet-buffer
WaveletBufferA universal  C++ compression library based on wavelet transformationFeaturesWritten in Modern C++One-side wavelet decomposition for vectors and matrices5 Daubechies Wavelets DB1-DB5Different denoising algorithmsFast and efficient compression withMatrixCompressorCross-platformRequirementsCMake >= 3.16C++20 compilerconan >= 1.56, < 2.0BindingsPythonUsage Example#include<wavelet_buffer/wavelet_buffer.h>usingdrift::Signal1D;usingdrift::WaveletBuffer;usingdrift::WaveletParameters;usingdrift::WaveletTypes;usingDenoiseAlgo=drift::ThresholdAbsDenoiseAlgorithm<float>;intmain(){Signal1Doriginal=blaze::generate(1000,[](autoindex){returnstatic_cast<float>(index%100);});std::cout<<"Original size: "<<original.size()*4<<std::endl;WaveletBufferbuffer(WaveletParameters{.signal_shape={original.size()},.signal_number=1,.decomposition_steps=3,.wavelet_type=WaveletTypes::kDB1,});// Wavelet decomposition of the signal and denoisingbuffer.Decompose(original,DenoiseAlgo(0,0.3));// Compress the bufferstd::stringarch;buffer.Serialize(&arch,16);std::cout<<"Compressed size: "<<arch.size()<<std::endl;// Decompress the bufferautorestored_buffer=WaveletBuffer::Parse(arch);Signal1Doutput_signal;// Restore the signal from wavelet decompositionrestored_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 InstallingOn 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 installOn 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 installOn 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 installIntegrationUsing cmake targetfind_package(wavelet_bufferREQUIRED)add_executable(programprogram.cpp)target_link_libraries(programwavelet_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(LAPACKREQUIRED)target_link_libraries(program${LAPACK_LIBRARIES})ReferencesDocumentationDrift Protocol- Protobuf Libraries to encode message in Drift infrastructureDrift Python Client- Python Client to access data ofPANDA|Drift