Saravanan666/pypi-org huggingface dataset:
Package: nemo-sz
Nemois a millimeter-wave Sunyaev-Zel’dovich galaxy cluster and
compact source detection package, originally developed for theAtacama Cosmology Telescopeproject,
and capable of analyzing maps that will be produced by theSimons Observatory.Documentation:https://nemo-sz.readthedocs.ioLicense:BSD 3-ClauseAuthors:Matt Hilton, with contributions from Simone Aiola, David Alonso,
Boris Bolliet, Matthew Hasselfield, Kevin Huffenberger, Toby Marriage, Niall MacCrann,
Sigurd Naess, Cristóbal Sifón, and Íñigo Zubeldia (not all reflected on GitHub).Installation:pip installnemo-szSupport:Please use theGitHub issues page,
and/or contactMatt Hilton.Nemois written inPythonand
providesseveral modulesthat
may be useful for analyzing ACT/SO data, in addition to the command-line programs provided
in the package.Nemoisnotthe pipeline used forHasselfield et al. (2013), but implements many
of the ideas presented there.It isthe package that was used to produce theACT DR3 cluster catalog,
and theACT DR5 cluster catalog.
All ACT data products are available fromLAMBDA.Nemois under active development, and not all documentation or example config files are up to date
(some may contain references to files that are not yet publicly available). The package also contains
some experimental features that are not necessarily well tested.If you need to acknowledge the use ofNemo, please citeHilton et al. (2021).
Package: nemo-text-processing
NeMo Text ProcessingIntroductionnemo-text-processingis a Python package for text normalization and inverse text normalization.DocumentationNeMo-text-processing (text normalization and inverse text normalization).TutorialsGoogle Collab NotebookDescriptionText_(Inverse)_Normalization.ipynbQuick-start guideWFST_TutorialIn-depth tutorial on grammar customizationGetting helpIf you have a question which is not answered in theGithub discussions, encounter a bug or have a feature request, please create aGithub issue. We also welcome you to directly open apull requestto fix a bug or add a feature.InstallationConda virtual environmentWe recommend setting up a fresh Conda environment to install NeMo-text-processing.condacreate--namenemo_tnpython==3.8
condaactivatenemo_tn(Optional) To usehybrid text normalizationinstall PyTorch using theirconfigurator.conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorchNOTE:The command used to install PyTorch may depend on your system.PipUse this installation mode if you want the latest released version.pip install nemo_text_processingNOTE:This should work on any Linux OS with x86_64. Pip installation on MacOS and Windows are not supported due to the dependencyPynini. On a platform other than Linux x86_64, installing from Pip tries to compile Pynini from scratch, and requires OpenFst headers and libraries to be in the expected place. So if it's working for you, it's because you happen to have installed OpenFst in the right way in the right place. So if you want to Pip install Pynini on MacOS, you have to have pre-compiled and pre-installed OpenFst. The Pynini README for that version should tell you which version it needs and what--enable-fooflags to use.
Instead, we recommend you to use conda-forge to install Pynini on MacOS or Windows:conda install -c conda-forge pynini=2.1.5.Pip from sourceUse this installation mode if you want the a version from particular GitHub branch (e.g main).pip install Cython
python -m pip install git+https://github.com/NVIDIA/NeMo-text-processing.git@{BRANCH}#egg=nemo_text_processingFrom sourceUse this installation mode if you are contributing to NeMo-text-processing.git clone https://github.com/NVIDIA/NeMo-text-processing
cd NeMo-text-processing
./reinstall.shNOTE:If you only want the toolkit without additional conda-based dependencies, you may replacereinstall.shwithpip install -e .with the NeMo-text-processing root directory as your current working director.ContributingWe welcome community contributions! Please refer to theCONTRIBUTING.mdfor guidelines.Citation@inproceedings{zhang21ja_interspeech,
  author={Yang Zhang and Evelina Bakhturina and Boris Ginsburg},
  title={{NeMo (Inverse) Text Normalization: From Development to Production}},
  year=2021,
  booktitle={Proc. Interspeech 2021},
  pages={4857--4859}
}

@inproceedings{bakhturina22_interspeech,
  author={Evelina Bakhturina and Yang Zhang and Boris Ginsburg},
  title={{Shallow Fusion of Weighted Finite-State Transducer and Language Model for
Text Normalization}},
  year=2022,
  booktitle={Proc. Interspeech 2022}
}LicenseNeMo-text-processing is underApache 2.0 license.
Package: nemo-toolkit
NeMo (NeuralModules) is a toolkit for creating AI applications built aroundneural modules, conceptual blocks of neural networks that taketypedinputs and producetypedoutputs.NeMo Coreprovides common APIs all modules and models have to implement.NeMo CollectionsASR - collection of modules and models for building speech recognition networksTTS - collection of modules and models for building speech synthesis networksNLP - collection of modules and models for building NLP networksVision - collection of modules and models for building computer vision networksMultimodal - collection of modules and models for building multimodal networks
Package: nemoview
NEMOVIEW - Visualization tool for NEMO ensembles.How to install it?The easiest way to install is to use pip:pip install nemoviewThis will install the latest released version.To install the version with the latest commit, run:pip install git+https://github.com/LeonardoESousa/nemoviewNOTE: It is recommended that you installNEMOviewwithin a virtual enviroment.How to use it?Once installed, use the command:nemoviewThe application will open in your browser.TutorialFor details on the analyses available inNEMOview, check thetutorial.
Package: nem-py
NEMpyHigh-level python wrapper for working with NEM cryptocurrencies ecosystem (only Symbol supported)Implemented on the basis symbol projectcore sdk python libraryPossibilitiesCreating a wallet with profiles and accounts. Importing account by mnemonic.Using a wallet to send funds, message (plain/encrypted), view activity history and balanceBlockchainmonitoringviawebsocketAbility to use all the above in third-party products and servicesGetting StartedThis is an example of how you can set up your project locally.
For up and running the wrapper locally please follow the next simple example steps.PrerequisitesTo work with the project, you will needpipenv tool.pipinstallpipenvInstallingInstall and update using pip:pipinstallnem-pyA Simple ExampleAttention!The example below is intended to demonstrate the ease of use, but it isnot secure! Use this code only on theNetworkType.TEST_NETfromnempy.user_dataimportAccountDatafromnempy.engineimportXYMEnginefromnempy.sym.networkimportNetworkTypefromnempy.sym.constantsimportFeesPRIVATE_KEY='<YOUR_PRIVATE_KEY>'PASSWORD='<YOUR_PASS>'account=AccountData.create(PRIVATE_KEY,NetworkType.TEST_NET).encrypt(PASSWORD)engine=XYMEngine(account)entity_hash,status=engine.send_tokens(recipient_address='TDPFLBK4NSCKUBGAZDWQWCUFNJOJB33Y5R5AWPQ',mosaics=[('@symbol.xym',0.1),],message='Hallo NEM!',password=PASSWORD,fee_type=Fees.SLOWEST)print(status.name,status.value)You can get funds for the balance for testing in theFaucet.Additionaldocumentationcan be foundhereCommand-line interface (CLI)You can get acquainted with the capabilities of the CLI interfacehereWorking withpipenvenvironmentClone the repositorygit clone https://github.com/denjas/nempy.gitGo to the directory with the projectcd nempyInstall virtualenv packagepip install pipenvSetting up a virtual environmentpipenv install --devInstalling a nem-py package into the environmentpipenv run pip install .orpipenv run pip install -e .for development mode.Running CLI utilitypipenvrunnempy-cli.pyTestingFollow the previous steps to set up your environment.Running testspipenv run testsorpipenv run tests --cov=nempyto assess coverageVersion NumbersVersion numbers will be assigned according to theSemantic Versioningscheme.
This means, given a version number MAJOR.MINOR.PATCH, we will increment the:MAJOR version when we make incompatible API changes,MINOR version when we add functionality in a backwards compatible manner, andPATCH version when we make backwards compatible bug fixes.
Package: nem-python
nem-python==========nem-python is simple NEM library.[README-JP.md](README-JP.md)Feature-------* Do **not** require NIS running on local.* You can sign on **offline**.* Useful transaction builder.* Useful simplify transaction.Require-------Python3 (>=3.5)HowToUse-----```pythonfrom nem_python.nem_connect import NemConnectnem = NemConnect(main_net=True)nem.start()```[HOWTOUSE.md](HOWTOUSE.md)Samples------Look test folder.Author------[@namuyan_mine](http://twitter.com/namuyan_mine/)Licence-------MIT
Package: nemreader
nem-readerThe Australian Energy Market Operator (AEMO) defines aMeter Data File Format (MDFF)for reading energy billing data.
This library sets out to parse these NEM12 (interval metering data) and NEM13 (accumulated metering data) data files into a useful python object, for use in other projects.Read the documentation on ReadTheDocs!InstallInstall and update usingpip:pipinstallnemreader
Package: nemreport
energy-reportGenerate energy report from NEM meter data filesEnergy DataFirst, request your energy data in NEM12 format from your DNSP (eg.ErgonorEnergex).UsageInstall python and then this library with:python-mpipinstallnemreportCopy your NEM12 file(s) into adata/folder and then run:python-mnemreportbuildOutputThis will then open a report with analysis of your data in a browser.
Package: nemsec
Создайте новый файл Python с расширением.py, напримерmain.py.Скопируйте предоставленный код и вставьте его в файлmain.py.Убедитесь, что у вас есть файлapi.pyв том же каталоге, где находитсяmain.py. Внутри файлаapi.pyдолжны быть определены классIOTAAPIи функцияis_malicious_packet. Убедитесь, что эти определения присутствуют и содержат нужный функционал.Замените'your_device_eui','your_app_eui'и'your_app_key'в коде на соответствующие значения для вашего устройства и приложения.Реализуйте функциюprocess_data(received_data), которая будет обрабатывать полученные данные от LoRaWAN. Добавьте нужную логику обработки данных внутри этой функции.Если вы хотите обработать вредоносные пакеты, создайте файлmalicious_keywords.txtв том же каталоге, где находитсяmain.py. Внутри файлаmalicious_keywords.txtдобавьте ключевые слова, которые будут использоваться для определения вредоносных пакетов. Каждое ключевое слово должно быть на отдельной строке.Запустите файлmain.py, например, в интерпретаторе Python или среде разработки, которую вы используете.После запуска код будет выполняться в бесконечном цикле. Он будет получать данные от LoRaWAN с помощьюiot_api.receive_data(). Затем он будет проверять полученные данные на наличие вредоносных пакетов с помощью функцииis_malicious_packet(). Если данные являются вредоносными, будет вызвана функцияhandle_malicious_packet(). В противном случае данные будут обрабатываться функциейprocess_data()вопросы или потребуется дополнительная помощь, пожалуйста, дайте мне знать!
Package: nemspy
NEMSpyNEMSpy generates configuration files (nems.configure,config.rc,model_configure,atm_namelist.rc)
for coupled modeling applications run with a compiled NEMS binary (not included).pipinstallnemspyNEMS implements
theNational Unified Operational Prediction Capability (NUOPC), and
configuration files built for NEMS will also work for most NUOPC applications.Documentation can be found athttps://nemspy.readthedocs.ioorganization / responsibilityNEMSpy is developed by theCoastal Marine Modeling Branch (CMMB)of the Office of Coast Survey (OCS), a part of theNational Oceanic and Atmospheric Administration (NOAA), an agency of the United States federal government.Zachary Burnett (lead) -zachary.burnett@noaa.govSaeed Moghimi -saeed.moghimi@noaa.govJaime Calzada (past)usagefromdatetimeimportdatetime,timedeltafrompathlibimportPathfromnemspyimportModelingSystemfromnemspy.modelimportADCIRCEntry,AtmosphericForcingEntry,WaveWatch3ForcingEntry# directory to which configuration files should be writtenoutput_directory=Path(__file__).parent/'nems_configuration'# directory containing forcingsforcings_directory=Path(__file__).parent/'forcings'# model run timestart_time=datetime(2020,6,1)duration=timedelta(days=1)end_time=start_time+duration# returning interval of main run sequenceinterval=timedelta(hours=1)# model entriesocean_model=ADCIRCEntry(processors=11,Verbosity='max',DumpFields=False)atmospheric_mesh=AtmosphericForcingEntry(filename=forcings_directory/'wind_atm_fin_ch_time_vec.nc',processors=1)wave_mesh=WaveWatch3ForcingEntry(filename=forcings_directory/'ww3.Constant.20151214_sxy_ike_date.nc',processors=1)# instantiate model system with model entriesnems=ModelingSystem(start_time=start_time,end_time=end_time,interval=interval,ocn=ocean_model,atm=atmospheric_mesh,wav=wave_mesh,)# form connections between modelsnems.connect('ATM','OCN')nems.connect('WAV','OCN')# define execution ordernems.sequence=['ATM -> OCN','WAV -> OCN','ATM','WAV','OCN',]# write configuration files to the given directorynems.write(directory=output_directory,overwrite=True,include_version=True)outputnems.configure# `nems.configure` generated with NEMSpy 1.0.0# EARTH #EARTH_component_list:ATMWAVOCNEARTH_attributes::Verbosity=off::# ATM #ATM_model:atmeshATM_petlist_bounds:00ATM_attributes::Verbosity=off::# WAV #WAV_model:ww3dataWAV_petlist_bounds:11WAV_attributes::Verbosity=off::# OCN #OCN_model:adcircOCN_petlist_bounds:212OCN_attributes::Verbosity=maxDumpFields=false::# Run Sequence #runSeq::@3600ATM->OCN:remapMethod=redistWAV->OCN:remapMethod=redistATMWAVOCN@::model_configure# `model_configure` generated with NEMSpy 1.0.0total_member:1print_esmf:.true.namelist:atm_namelist.rcPE_MEMBER01:13start_year:2020start_month:6start_day:1start_hour:0start_minute:0start_second:0nhours_fcst:24RUN_CONTINUE:.false.ENS_SPS:.false.config.rc# `config.rc` generated with NEMSpy 1.0.0atm_dir:~/forcingsatm_nam:wind_atm_fin_ch_time_vec.ncwav_dir:~/forcingswav_nam:ww3.Constant.20151214_sxy_ike_date.ncrelated projectsNOAA-EMC/NEMSesmf-org/esmfnoaa-ocs-modeling/ADC-WW3-NWM-NEMS
Package: nemtpy
nemtpy - это мощная библиотека, разработанная компанией NemtyrevAI, которая предоставляет эффективные инструменты для хранения, обработки и анализа массивов данных различных типов. Библиотека написана на C++ и предоставляет питонический интерфейс для удобного использования из Python. Так-же предоставляет мощные инструменты для создания и обучения искусственного интеллекта (ИИ). Библиотека специально разработана для облегчения процесса разработки ИИ-систем и предлагает широкий набор функций и высокоуровневых абстракций, позволяющих создать и обучить простые ИИ-моделиИспользуя библиотекуnemtpy, вы можете заменить популярные решения, такие как NumPy, Pandas и SciPy. nemtpy это мощная библиотека, разработанная  NemtyrevAI, которая предоставляет эффективные инструменты для хранения, обработки и анализа массивов данных различных типов. Библиотека написана на C++ и предоставляет питонический интерфейс для удобного использования из Python.
Package: nemusicapi
NEmusicApi一个网易云音乐的Api框架项目介绍本项目基于网易云音乐API，实现了部分网易云音乐的功能，包括但不限于：搜索歌曲获取歌曲详情获取歌曲url下载歌曲食用使用教程使用pip安装pip install nemusicapi导入包，创建apifromNEmusicApiimportApiapi=Api()然后就可以愉快的使用了举个栗子例子demo.py注意事项创建api的时候，不输入cookie或者cookie对应的账号没有vip，都有可能导致获取信息不完整，需要自己测试创建api的时候，不输入download_dir，在使用download_song方法的时候会raise异常
Package: nemwriter
nem-writerWrite meter readings to AEMO NEM12 (interval metering data) and NEM13 (accumulated metering data) data filesAccumulated Data (NEM13)fromdatetimeimportdatetimefromnemwriterimportNEM13m=NEM13(to_participant='123')ch=m.add_reading(nmi='123',nmi_configuration='E1B1B2',register_id='1',nmi_suffix='E1',previous_read=412,previous_read_date=datetime(2017,1,1),previous_quality_method='A',current_read=512,current_read_date=datetime(2017,2,1),current_quality_method='A',quantity=100,uom='kWh')output=m.output_csv(file_path='output.csv')Will output:100,NEM13,201701010101,,123
250,123,E1B1B2,1,E1,,,E,412,201701010000,A,,,512,201702010000,A,,,100,kWh,,,
900Interval Data (NEM12)fromdatetimeimportdatetimefromnemwriterimportNEM12m=NEM12(to_participant='123')readings=[# read end, read value, quality method, event code, event desc[datetime(2004,4,18,0,30),10.1,'A',79,'Power Outage Alarm'],[datetime(2004,4,18,1,0),11.2,'A'],[datetime(2004,4,18,1,30),12.3,'A'],[datetime(2004,4,18,2,0),13.4,'A'],]ch=m.add_readings(nmi='123',nmi_configuration='E1B1B2',nmi_suffix='E1',uom='kWh',readings=readings)output=m.output_csv(file_path='output.csv')Will output:100,NEM12,201701010101,,123
200,123,E1B1B2,,E1,,,kWh,30,
300,20040418,10.1,11.2,12.3,13.4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,V,,,,
400,1,1,A,79,Power Outage Alarm
400,2,48,A,,
900Alternatively, save as a compressed csv in a zip file.output=m.output_zip(file_path='output.zip')From Pandas DataFrameIf you create a pandas DataFrame, for example:num_intervals=288index=[datetime(2004,4,1)+timedelta(minutes=5*x)forxinrange(1,num_intervals+1)]e1=[randrange(1,10)forxinrange(1,num_intervals+1)]e2=[randrange(1,5)forxinrange(1,num_intervals+1)]s1=pd.Series(data=e1,index=index,name="E1")s2=pd.Series(data=e2,index=index,name="E2")df=pd.concat([s1,s2],axis=1)print(df)E1  E2
2004-04-01 00:05:00   2   3
2004-04-01 00:10:00   8   3
2004-04-01 00:15:00   7   2
2004-04-01 00:20:00   4   3
2004-04-01 00:25:00   3   4
...                  ..  ..
2004-04-01 23:40:00   9   2
2004-04-01 23:45:00   1   1
2004-04-01 23:50:00   6   2
2004-04-01 23:55:00   7   1
2004-04-01 00:00:00   4   2You can easily output the dataframe to a NEM12 file:m=NEM12(to_participant='123')m.add_dataframe(nmi='123',interval=5,df=df,uoms={'E1':'kWh','E2':'kWh'})output=m.output_csv(file_path='output.csv')If your DataFrame has aQualityandEventDesccolumn, they will also be handled appropriately.
Package: nendo
The AI audio tool suite for developers, powering next-gen audio applications.Website:okio.aiDocumentation:okio.ai/docsNendo Platform:RepositoryNendo core is the AI audio tool suite allowing you to effortlessly develop audio apps that amplify efficiency & creativity across all aspects of audio production.Features-Requirements-Installation-Usage-Plugins-ContributingFeaturesEasy to use, lightweight framework to develop AI audio applications fast.Integrated essentials for audio processing and library management.An extensible plugin architecture and growing ecosystem of AI Audio plugins.Easily combinable tools that together address a wide range of use cases.Support for storing, managing and retrieving embedding vectors.RequirementsNendo core requires Python version 3.8, 3.9 or 3.10.It is recommended to use avirtual environmentfor installing nendo core, in order to avoid dependency conflicts. You can use your favorite virtual environment management system, likeconda,poetry, orpyenvfor example.Furthermore, the following software packages need to be installed in your system:Ubuntu:sudo apt-get install ffmpeg libsndfile1 libportaudio2Mac OS:brew install ffmpeg libsndfile portaudioWindowsWindows support is currently under development. For the time being, we highly recommend usingWindows Subsystem for Linuxand then following the linux instructions. If you still want to try to get Nendo Core to work natively on Windows, you will need to install the following software packages:ffmpeg,libsndfile, andportaudioInstallationYou can install Nendo Core directly viapip:pipinstallnendoThen you can run nendo in your python shell, notebook or application as follows:fromnendoimportNendonendo=Nendo()... and just like that, you're ready to go! Now, there are multiple ways to configure Nendo, refer to therelevant documentation pagesfor more information.UsageFor example,install the musicgen nendo plugin.And then run it using nendo:fromnendoimportNendond=Nendo(plugins=["nendo_plugin_musicgen"])songs=nd.plugins.musicgen(prompt="funky 70s disco",bpm=120)songs[0].export("funky_disco.mp3")Please refer to thedocumentationto learn more about how to use nendo.PluginsNendo thrives on its richpluginecosystem. There are plugins for many different audio processing tasks and the list is growing:Audio GenerationSource SeparationAudio AnalysisVoice GenerationMidi GenerationAudio TranscriptionAudio Post-ProcessingAudio QuantizationAudio Loop-ExtractionIf you want to develop your own plugin for nendo, consult theplugin development documentation, you'll be surprised how simple it is.ContributorsFelix LorenzAaron AbebeSamimSteve HiehnJulian LenzPhilipp BraunMichal HoWant to be part of the AI audio revolution? All contributions are welcome! Check out ourcontribution guideto learn more about how to develop with and for nendo.
Package: nendo-plugin-caption-lpmusiccaps
Nendo Plugin Caption LP-MusicCapsAn audio captioning plugin based on LP-MusicCaps.FeaturesGenerate captions for all types of audioCaption your audio library and make it searchable by using the other nendo search pluginsRequirementsThis plugin requires the manual installation of theLP-MusicCapsrepository from git, run:gitclonehttps://github.com/seungheondoh/lp-music-caps.gitcdlp-music-caps
pipinstall-e.For more information, please refer to theLP-MusicCaps repository.InstallationInstall Nendopip install nendo-plugin-caption-lpmusiccapsUsageTake a look at a basic usage example below.
For more detailed information, please refer to thedocumentation.>>>fromnendoimportNendo>>>nd=Nendo(plugins=["nendo_plugin_caption_lpmusiccaps"])>>>track=nd.library.add_track(file_path="path/to/file.mp3")>>>nd.plugins.caption_lpmusiccaps(track=track)>>>track.get_plugin_value("caption")ContributingVisit our docs to learn all about how to contribute to Nendo:ContributingLicenseNendo: MIT LicenseLP-MusicCaps: CC-BY-NC 4.0 license.Pretrained models: The weights are released under the CC-BY-NC 4.0 license
Package: nendo-plugin-classify-core
Nendo Plugin Classify CoreAutomatic music information retrieval (based onessentia).FeaturesExtract musical features from aNendoTrackor aNendoCollectionUse descriptive features to filter, search and sort your libraryExtract rich features to annotate datasets for training custom modelsInstallationInstall nendopip install nendo-plugin-classify-coreNote: Installing this plugin together with another nendo plugin that usesessentia, you are likely to run into the runtime errorError: module 'essentia.standard' has no attribute 'TensorflowPredictEffnetDiscogs'. See the corresponding entry in thetroubleshooting guidebelow for instructions on how to fix this issue.UsageTake a look at a basic usage example below.
For more detailed information, please refer to thedocumentation.For more advanced examples, check out the examples folder.
or try it in colab:fromnendoimportNendo,NendoConfignd=Nendo(config=NendoConfig(plugins=["nendo_plugin_classify_core"]))track=nd.library.add_track(file_path='/path/to/track.mp3')track=nd.plugins.classify_core(track=track)data=track.get_plugin_data(plugin_name="nendo_plugin_classify_core")print(data)tracks_with_filtered_tempo=nd.library.filter_tracks(filters={"tempo":(170,180)},plugin_names=["nendo_plugin_classify_core"],)assertlen(tracks_with_filtered_tempo)==1TroubleshootingEssentia can't find the embedding modelWhen I try to run the plugin, I get the following error:Failedtoimportplugin'nendo_plugin_classify_core'.Error:module'essentia.standard'hasnoattribute'TensorflowPredictEffnetDiscogs'This is due to the fact that you haveessentiaandessentia-tensorflowinstalled at the same time, possibly because you installed another nendo plugin that usesessentia, like e.g.nendo_plugin_quantize_core. The fix in this case is to reinstall essentia, this time only installing theessentia-tensorflowpackage:pipuninstall-yessentiaessentia-tensorflow
pipinstallessentia-tensorflowContributingVisit our docs to learn all about how to contribute to Nendo:ContributingLicenseNendo: MIT LicenseEssentia: Affero GPLv3 license