Saravanan666/pypi-org huggingface dataset:
Package: vicommithooks
vicommithooksA set of common LabVIEW project requirements to prevent checking unshippable code into the repo.Usagepip install vicommithooks
vicommithooks --help
usage: vicommithooks [-h] [-r] [--source-only] [--min-version MIN_VERSION]
                     [--max-version MAX_VERSION] [--not-prerelease]
                     file

positional arguments:
  file

optional arguments:
  -h, --help            show this help message and exit
  -r, --recursive       Recursively searched the path for all VIs.
  --source-only         Asserts the VI(s) are set to source only
  --min-version MIN_VERSION
                        Asserts that the major version of LabVIEW the VI was
                        saved with is greater than or equal to this value
  --max-version MAX_VERSION
                        Asserts that the major version of LabVIEW the VI was
                        saved with is less than or equal to this value
  --not-prerelease      Asserts that the version of LabVIEW the VI was saved
                        with is not a prerelease versionContributingFeel free to post PRs to improve this.
Package: vicon-nexus-unity-stream-py
OverviewPython script to stream data from vicon nexus to unitySetupRequirementsPython 3.8+InstallationInstall it directly into an activated virtual environment:$ pip install vicon_nexus_unity_stream_pyor add it to yourPoetryproject:$ poetry add vicon_nexus_unity_stream_pyUsageAfter installation, the package can be used as a cli tool:$ vicon-nexus-stream --help

Usage: vicon-nexus-stream [OPTIONS] COMMAND [ARGS]...

Options:
  --help  Show this message and exit.

Commands:
  server  Connects to the vicon and streams the data...
  stream  Instead of connecting to vicon, streams data...
  test    Test if connection is workingCreditsThis project was generated withcookiecutterusingjacebrowning/template-python.
Package: vicorrect
Correct Vietnamese Sentence toolDescriptionAdding accents vietnamese sentence toolIntsallationpip install vicorrectExamples:fromvicorrect.modelimportCorrectVietnameseSentencedataset="""trời buồn trời đổ cơn mưachim khôn chim đậu cành đa"""corrector=CorrectVietnameseSentence(verbose=False)corrector.fit(dataset.splitlines())testcase=["troi buon","com chim dau","chim dau canh da troi do mua"]print(corrector.predict(testcase))AuthorHanh. Le Van
Package: vicroads-transport-api
VicRoads Data Exchange Python API WrapperThis Python package provides an asynchronous API wrapper for accessing transportation updates from the VicRoads Data Exchange platform. The wrapper supports retrieval of various transportation data, including service alerts, trip updates, and vehicle positions for metro trains, buses, and trams.InstallationYou can install the package usingpip:pipinstallvicroads-data-exchange-apiUsageInitializationFirst, import the required modules, create a DataExchangeClient instance, and instantiate the GTFS_R class:fromvicroads_data_exchange_apiimportDataExchangeClient,GTFS_R# Create a DataExchangeClient instanceclient=DataExchangeClient('your_api_key_here')# Initialize GTFS_R with the DataExchangeClient instancegtfs_api=GTFS_R(client)Retrieving Service Alertsyarra_service_alerts=awaitgtfs_api.yarra_trams_service_alerts()metro_train_alerts=awaitgtfs_api.metro_trains_service_alerts()Retrieving Trip Updatesbus_updates=awaitgtfs_api.metro_bus_trip_updates()tram_updates=awaitgtfs_api.yarra_trams_trip_updates()Retrieving Vehicle Positionstrain_positions=awaitgtfs_api.metro_trains_vehicle_positions()tram_positions=awaitgtfs_api.yarra_trams_vehicle_positions()DocumentationFor further details and available methods, refer to the API Documentation.ContributingFeel free to contribute by opening issues or submitting pull requests. We welcome improvements, bug fixes, or additional features.
LicenseLicenseThis project is licensed under the MIT License - see the LICENSE file for details.
Package: victorbaptistalemos-libpythonpro
victorbaptistalemos_libpythonproMódulo para exemplificar construção de projetos Python no curso PyTools.Nesse curso é ensinado como contribuir para projetos de código aberto.Link para o cursoPython Pro.Este projeto tem como base o repositóriopythonprobr/libpythonpro.Suportada a versão 3 do Python.Tópicos a serem abordados:GitClone:Copia um repositório através do link HTTPS ou SSH.Fork:Cria uma cópia a partir de outro repositório.
Utilizado para fazer melhorias em projetos de
código aberto que você não tenha permissão
para modificar.Branch:Utilizada para fazer alterações independentes no código.
Caso a modificação na branch criada dê algum priblema,
esta pode ser excluída sem alterar o código principal.Pull request:Utilizada para solicitar a inclusão de uma alteração
código na branch principal.Issues:Utilizada para solicitar melhorias no código.Feature branch:Utilizada por outro contribuidor para fazer melhorias em seu código.Tag:Marca a distribuição de uma release.VirtualenvCriar:python3 -m venv .venvAtivar:source .venv/bin/activateDesativar:deactivatePipInstalar:pip install -r requirements.txtAtualizar:pip install --upgrade requestsDesinstalar:pip uninstall -r requirements.txt -yRequirements:pip freeze > requirements.txtFlake8Instalar:pip install flake8Configurar:#Crieumarquivo.flake8#Insiranoarquivo.flake8asconfiguraçõesabaixo(semo#)#[flake8]#max-line-length=120#exclude=.venvConferir qualidade do código:flake8Travis CIPyUpPyPIInstalação do twine:pip install twineCriação de distribuição:python setup.py sdistEnvio da distribuição ao PyPI:twine upload dist/*PyTestInstalaçãopip install pytestCriando testes e a utilização da palavra reservadaassert.Cobertura de testes:Desenvolvimento Orientado a Testes (TDD).Parametrização de testes com o decorator@pytest.mark.parametrize.Técnica do baby steps (desenvolvimento passo a passo).Testes de Exceções.PyTest Fixtures.Utilização da palavra reservadayield.Utilização e escopo do arquivoconftest.py.Injeção de dependências.ClasseMock()da bibliotecaunittest.mock.Utilização da APIpytest-mocke seu métodomocker.patch().PipenvInstalação.Desativar o ambiente virtual.deactivateFora do ambiente virtual:pip install pipenvEditar.baschrcacrescentando:export PIPENV_VENV_IN_PROJECT=1Reiniciar terminal para obter os efeitos desejados.Instalação de dependências:pipenv install requestsInstalação de dependências para desenvolvedores:pipenv install --dev flake8 twine pytest coverage pytest-cov pytest-mockAlteração de scripts do arquivo.travis.yml.Alteração de scripts do arquivo.pyup.yml.Remoção dos arquivosrequirements.txterequirements-dev.txt.Revisão para iniciar o curso doMódulo Django
Package: victoria
V.I.C.T.O.R.I.A.VeryImportantCommands forToilOptimization:ReducingInessentialActivities.Victoria is the SRE toolbelt—a single CLI with multiple pluggable
subcommands for automating any number of 'toil' tasks that inhibit SRE
productivity.FeaturesPlugin architecture for easy extensionStore config for plugins in cloud storageAzure Blob StorageEncrypt secret config data at rest using cloud encruption providerAzure Key VaultPrerequisitesPython 3.6+PipAzure CLIInstallationpip install -U victoriaDocumentationThe documentationcan be found here.LicenseVictoria is licensed under the MIT license.ContributionBug reportsYou can submit a bug report here.Feature requestsYou can request a new feature here.Vulnerability reportsWe prefer vulnerabilities to be reported in private so as to minimise their
impact (so we can fix them before they are exploited!). To this end, please
email any security vulnerability reports to 'sre@glasswallsolutions.com'.
We would appreciate it if you use the issue template in the link below.
All vulnerabilities will be acknowledged within one business day.You can publicly report a security vulnerability here.Pull requestsWe accept pull requests! To contribute:Pick up an unassigned issue fromour issue board.
Assign yourself to the issue so other people know you're working on it.Work on your code in a feature branch that's got a descriptive name (likerework-fancy-integrator).Follow theGoogle style guide for Python.
Every piece of code needs to be documented withGoogle-style docstrings.
We usepylintto lint our code.
We run pylint without the 'convention' and 'refactor' message classes.
You can lint your code with:poetry run pylint victoria --disable="C,R".
We useyapfto automatically format our code. We recommend having it
format the code whenever you save.Make commits for each part of your pull request. Try not to make too many (if it's a small issue you may only need one).
We try to usethe imperative moodin commit message subjects.We expect all new code to have at least 80% test coverage. This is enforced byCodecov.To run tests locally and check coverage, use:poetry run pytest tests/ --cov=victoria.When ready to merge, create a pull request from your branch into master.Make sure you link your pull request to the issue(s) it addresses.TheCI buildwill run
for your pull request. If it fails then it cannot be merged. Inspect the output, figure
out why it failed, and fix the problem.
The CI build will lint your code, run tests, and send coverage/code to Codecov
andSonarCloud.Someone will review your pull request and suggest changes if necessary.When everything is signed off, your pull request will be merged! Congrats.Development guidePrerequisitesPython 3.6+PoetryQuick startClone the repo.Runpoetry install.You're good to go.
Package: victor-services-splitter
SplitterHow to install or update the lib# The .h5 models are installed along with the pip installation. 
    # There's no need to hardcode their path anywhere.
pip install git+https://gitlab.com/gpam/victor/SERVICES/splitter.git
# Or
pip install victor-services-splitterHow to uninstall the libpip uninstall victor-services-splitterHow to generate predictionsfrom splitter.model import ModelSplitter

# Setup the threading of the TensorFlow model operations
# (...)

model_splitter = ModelSplitter("path/to/model/page/image/features",
                               "path/to/model/page/classification")

# Create the image list. See the supported image types section
image_list = [...]


prediction_list = model_splitter.predict_pdf_pages(
    image_list
    )
# [1, 0, 0, 1, 1, 1, 0]

grouped_prediction_list = model_splitter.predict_pdf_pages(
    image_list,
    has_to_group_predictions=True
    )
# [[1, 0, 0], [1], [1], [1, 0]]

precise_prediction_list = model_splitter.predict_pdf_pages(
    image_list,
    prediction_decimals=3
    )
# [1.0, 0.312, 0.0, 0.963, 0.534, 0.744, 0.23]Setup the threading of the TensorFlow model operations.Read more at theTensorFlow docs about threading.import tensorflow as tf

# a value of 0 means the system picks an appropriate number.
tf.config.threading.set_intra_op_parallelism_threads(
    num_threads=0
    )

# a value of 0 means the system picks an appropriate number.
tf.config.threading.set_inter_op_parallelism_threads(
    num_threads=0
    )Supported image types- Pillow
- Base64
- Bytes
- Image path ending with '.jpg', '.png' or '.jpeg'
Package: victor-smart-kill
Victor Smart-KillStatus and history of Victor Smart-Kill WI-FI electronic mouse and rat traps from VictorPest.com can be accessed with this async Python wrapper module. The wrapper uses the same API as the Victor Pest APP.Getting startedThis example gets a list of trap information and trap history for the first trap:fromvictor_smart_killimportVictorApi,VictorAsyncClientasyncwithVictorAsyncClient("username","password")asclient:api=VictorApi(client)traps=awaitapi.get_traps()history=awaitapi.get_trap_history(traps[0].id)API list methodsget_activity_logs()get_mobile_apps()get_operators()get_profiles()get_traps()get_trap_history()get_users()Single item methods.Each list method usually has corresponding metods to get a single item by id and/or url. You can find the id or url in the result from list mehods.
Package: victor-temas
Victor TemasModo de usoImporte a classe VictorThemes, do arquivo victor_temas/victor_themes.py
Instancie a classe VictorThemes com os parametros: tfidf, vectorizer e o DataFrame de temas.
O método predict recebe um texto não processado e retorna uma lista de resultados sobre o texto para cada tema, cada elemento é uma tupla contendo:Pertinência não limitadaSimilaridade mínima entre o texto de entrada e os textos do tema analisadoSimilaridade média entre o texto de entrada e os textos do tema analisadoSimilaridade máxima entre o texto de entrada e os textos do tema analisadoA lista de temas sendo analisadaO número de amostras contidas nesse tema.Os parâmetros opcionais da funcao predict são:ordenar_por. Domínio:['pertinencia','similaridade'], valor default:'pertinencia'.
Define a ordem de ordenacão da lista de saída do método predict.
Caso seja 'pertinencia', ordena os elementos por maiorlimiar_pert=-10000, limiar_sim=0Exemplo:from victor_temas import VictorThemes

vt=VictorThemes(TFIDF_PATH, VEC_PATH,THEMES_PATH)
vt.predict(texto)
Package: victron-ble-ha-parser
Victron BLE HA ParserOverviewThis project exists solely to provide an interface to the excellentvictron-bleproject suitable for use with a Home
Assistant integration. Thank you to Keshav Varma for maintaining thevictron-bleproject.DetailsThe parser module has one class,VictronBluetoothDeviceData, which parses Bleak advertisements and
updates itself with the sensor data from the advertisement. Each instance ofVictronBluetoothDeviceDatashould be fed advertisements from exactly one Victron device (based on
the MAC address) and the instance needs to be initiated with the encryption key of the device that
is going to be sending updates. If the encryption key is set correctly everything else will be
figured out automatically.There is a handy package from maintainers of the HA project calledsensor-state-datato make it
easy to automate ingesting sensor state updates like this. Unfortunately, we need to use a unit
(amp-hours) not supported by the module and my PR to include amp-hours was rejected because they are
not a common unit (https://github.com/Bluetooth-Devices/sensor-state-data/pull/47). For that
reason, we need a custom extension of sensor-state-data, which is contained in the
custom-sensor-state.py file.
Package: vid2aud
InstallationBefore Installation,vid2audrequiresoundscape_IRas dependency. Thus, installsoundscape_IRfirst in a Python>=3.7.0 environment.gitclonehttps://github.com/meil-brcas-org/soundscape_IR.gitcdsoundscape_IR
pipinstall-rrequirements.txtRun the following command to installvid2audfrom PyPI.pipinstallvid2audTo install the latest version, clone the repository and install the module with dependencies from the top-level folder.gitclonehttps://github.com/yijensun/vid2aud.gitcdvid2aud
pipinstall.--use-feature=in-tree-buildQuick startvid2audallows importing video from both Google drive and local directory.
In this guide, we import video from Google drive. Assign the folder ID of the Google drive folder that contains video infolder_idand the video format infile_format(The folder ID is a string of text after the last / in your folder's url).To import the video from local directory, usepathto assign the directory instead.fromvid2audimportvid2aud# loading video from Gdrivetest=vid2aud(folder_id='1r8OF_5Vu7p8tjdZwsf1maOcKHdmZWpuo',file_format='.mp4')# loading video from local directory#test = vid2aud(path='data/video/', file_format='.mp4')Choose the video file desired to convert byfile_noand assign file name and file format of the converted audio inaudio_nameandaudio_formatrespectively.#converting...test.convert(file_no=2,audio_name='GH020063',audio_format='.wav')By the functionaudio_visualizationinsoundscape_IR, we can plot the spectrogram of the converted audio.fromsoundscape_IR.soundscape_viewerimportaudio_visualization#Spectrogram of part of the converted audiocamera=audio_visualization('GH020063.wav',offset_read=74,duration_read=15,FFT_size=2048,window_overlap=0,plot_type='Spectrogram',f_range=[50,4000],prewhiten_percent=0)Currently ongoing developmentsBatch process videosBugs report and suggestionsIf you encounter any bug or issue, please contact Yi-Jen Sun viaelainesun442@gmail.com. Suggestions are also appreciated!
Package: vid2emoji
vid2EmojiConvert a video to... still a video but it's made of emojis.You will need:A video fileAn emoji font file. Your mac or windows computer has one. Unfortunately, Noto Emoji and Emoji
One fonts aren't well supported by Pillow, the image processing library used here.Example:https://www.youtube.com/watch?v=hLpJeg21-IwemojEncode used under GPLv3. Original available athttps://github.com/Ophelia-Heyes/emojEncodeInstallationInstall withpip install vid2emojiUsagevid2emoji <your video file>
Package: vid2frames
Converts a video to frames# ffmpeg needs to be installed!pipinstallvid2framesfromvid2framesimportconvert_video_to_framesallfiles=convert_video_to_frames(video=r"F:\newvidxx2.mp4",output_folder="f:\\outputvi",# folder will be created if it doesn't existsfps=24,fileprefix="videoframe",zfill=8,# videoframe_1.png -> videoframe_0000001.png)
Package: vid2gif
vid2gifConvert a video or multiple images into an optimised GIF.Suited for text such as console screen recordings.Requiresgifsicle,convertandffmpeg/avconvbinaries.Table of contentsInstallationLatest PyPI stable releaseLatest development release on githubChangelogUsageDocumentationContributionsLICENCEAuthorsInstallationLatest PyPI stable releasepipinstallvid2gifLatest development release on githubPull and install in the current directory:pipinstall-egit+https://github.com/casperdcl/vid2gif.git@master#egg=vid2gifChangelogThe list of all changes is available either on GitHub’s Releases:or on crawlers such asallmychanges.com.Usagevid2gif [–help | options] <outfile> <infile>…Documentation(Since 19 May 2016)vid2gif--help-h, --help     Print this help text and exit
-v, --version  Print version number and exit
-a=<a>, --avconv=<a>   FFMpeg executable in PATH [default: avconv]
-c=<c>, --convert=<c>  Conversion executable in PATH [default: convert]
-C=<C>, --cdepth=<C>   Colour conversion depth [default: 3]
-d=<d>, --dupdir=<d>   Directory to move duplicate input files into
                       [default: vid2gif_dup]
-D, --nodup    Don't move duplicates (ignores `dupdir`)
-g=<g>, --gifsicle=<g>
               gifsicle executable in PATH [default: gifsicle]
-i, --interactive      Pause and wait for the user to manually tweak images
                       after video -> image conversion
-l=<level>, --log=<level>
               Print various types of debugging information. Choices:
                       CRITICAL|FATAL
                       ERROR
                       WARN(ING)
                       [default: INFO]
                       DEBUG
                       NOTSET
-L, --log-trace        Print lots of debugging information (-D NOTSET)
-n, --just-print       Don't execute commands, just show them (dry-run, recon)
-T=<T>, --period=<T>   Frame period [default: 10]
-t=<t>, --tmpdir=<t>   Directory to use as scratch space
                       [default: vid2gif_tmp]ContributionsAll source code is hosted onGitHub.
Contributions are welcome.See theCONTRIBUTEfile for more information.LICENCEOpen Source (OSI approved):Citation information:AuthorsCasper da Costa-Luis (casperdcl)(Since 2 Nov 2016)
Package: vid2mp3
Commandline tool to download youtube flash videos as mp3.It communicates with listentoyoutube.com for this purpose.Vimeo.com, dailymotion.com, metacafe.com are also supported by this tool.InstallationNow it is an early release so if you want to install it in your system it requires su/superuser access.To install the script do thisOption 1 : Install via pip$ sudo pip install vid2mp3Option 2 : If you have downloaded the source$ sudo python setup.py installHow-to UseUse it as$ vid2mp3 [video-link]Releasesv0.1First stable release.Supports youtube.com.Supports vimeo.com.Supports dailymotion.com.Downloads the audio file(.mp3) into present working directory.v0.1.1More user friendly and verbose.Fixed wrong url exception.v0.1.2Added dependency link for dependency lxmlv0.1.3Fixed bug: UnicodeError handled when there’s a unicode problem.Reporting BugsPlease report bugs at github issue tracker:https://github.com/iamsudip/vid2mp3/issuesAuthoriamsudip <iamsudip@programmer.net>http://github.com/iamsudip
Package: vid2pdf
vid2pdfSimple helper utility to convert a video file to PDF image seriesExternal Requirementsvid2pdfrequires ffmpeg to be extracted to the/utils/ffmpegfolder. The latest version of ffmpeg can be downloaded fromffmpeg.org. Existing local ffmpeg installations are not currently supported.If not using a precompiled build, Python must be installed on your local machine. You can download the latest version of Python for your OS frompython.orgInstallationThis project utilizespoetryfor dependency & environment management. Clone or download this repository to your local machine and create a new environment:$cd<project_dir>
$poetryinstallThough it's recommended to utilizepoetry, the project may also be installed viapip:$cd<project_dir>
$pipinstall.Alternatively, prebuilt binaries are provided athttps://github.com/sco1/vid2pdf/releasesUsagevid2pdfcan be invoked using Python:$pythonvid2pdf.pyOr, if a prebuilt binary is present, this may be called directly$vid2pdf.exeInput ParametersUnless otherwise noted, all input parameters are prompted in the CLIinput_videoThe default behavior is to open a GUI dialog for the user to specify a the input video file. An optional-cliflag may be passed to bypass this GUI and instead prompt for the video file path in the CLI.output_dirPDF output directory. If this value is not specified, this defaults to the parent directory of the input video.start_timeVideo start time for capture, ashh:mm:ss.sss. If this value is not specified, the beginning of the video is used.end_timeVideo end time for capture, ashh:mm:ss.sss. If this value is not specified, the end of the video is used.Examples$pythonvid2pdf.py
Entertheoutputdirectorypath[X:\vid2pdf\test]:
Enterstarttime(hh:mm:ss.sss).Leaveblanktousethevideostart:
Enterendtime(hh:mm:ss.sss).Leaveblanktousethevideoend:00:00:01.000
<ffmpegoutputsnipped>
Loading30frames...100%|███████████████████████████████████████|29/29[00:00<00:00,852.82it/s]GeneratingPDF...done$pythonvid2pdf.py-cli
Enterthevideofilepath:X:\vid2pdf\test\test_video.mp4
Entertheoutputdirectorypath[X:\vid2pdf\test]:
Enterstarttime(hh:mm:ss.sss).Leaveblanktousethevideostart:
Enterendtime(hh:mm:ss.sss).Leaveblanktousethevideoend:00:00:01.000
<ffmpegoutputsnipped>
Loading30frames...100%|███████████████████████████████████████|29/29[00:00<00:00,852.82it/s]GeneratingPDF...done