Saravanan666/pypi-org huggingface dataset:
Package: wampli
WAMPliA command line interface for the WAMP protocol.IntroductionWAMPli allows you to call procedures, publish to topics, and
subscribe to topics from the comfort of your console.There's also a shell mode which starts an interactive console to
conveniently perform all previously mentioned operations.InstallUsing PIPpipinstallwampliManuallyFirst get the source code by either cloning the repo or downloading the
archive.From here there are two ways to proceed.Install using setup.py:This is basically the same as installing it from PyPI,
but you can pass the-eflag to get aneditable install.Runpip install .That's it.Install using pipenv:Installpipenvusingpip install pipenvInstall the dependencies usingpipenv install.You can now usepipenv run wamplito run wampli.
Which is a shortcut forpipenv run python -m wampliUsageWIP.
Usewampli -hto get some basic
help.
Package: wamu
wamuPythom utilities for working with Weights and Measures.PrecisionWherever possible,wamuattempts to maintain the precision of a conversion as opposed
to relying on floating point math.  For example, converting from meters per second to
kilometers per hour would be calculated as:kmph = (mps * 3600.0) / 1000.0However, the floating point division may result in loss of precision.  In such cases,wamuwill simplify the expression to:kmph = mps * 3.6
Package: wan-account
wan-accountSign Ethereum transactions and messages with local private keysRead more in thedocumentation on ReadTheDocs.View the change logQuickstartpipinstallwan-accountDeveloper SetupIf you would like to hack on wan-account, please check out theSnake Charmers
Tactical Manualfor information on how we do:TestingPull RequestsCode StyleDocumentationDevelopment Environment SetupYou can set up your dev environment with:gitclonegit@github.com:ethereum/wan-account.gitcdwan-account
virtualenv-ppython3venv
.venv/bin/activate
pipinstall-e.[dev]To run the integration test cases, you need to install node and the custom cli tool as follows:apt-getinstall-ynodejs# As sudo./tests/integration/ethers-cli/setup_node_v12.sh# As sudocdtests/integration/ethers-cli
npminstall-g.# As sudoTesting SetupDuring development, you might like to have tests run on every file save.Show flake8 errors on file change:# Test flake8when-changed-v-s-r-1wan_account/tests/-c"clear; flake8 wan_account tests && echo 'flake8 success' || echo 'error'"Run multi-process tests in one command, but without color:# in the project root:pytest--numprocesses=4--looponfail--maxfail=1# the same thing, succinctly:pytest-n4-f--maxfail=1Run in one thread, with color and desktop notifications:cdvenv
ptw--onfail"notify-send -t 5000 'Test failure ⚠⚠⚠⚠⚠' 'python 3 test on wan-account failed'"../tests../wan_accountRelease setupFor Debian-like systems:apt install pandocTo release a new version:makereleasebump=$$VERSION_PART_TO_BUMP$$How to bumpversionThe version format for this repo is{major}.{minor}.{patch}for stable, and{major}.{minor}.{patch}-{stage}.{devnum}for unstable (stagecan be alpha or beta).To issue the next version in line, specify which part to bump,
likemake release bump=minorormake release bump=devnum. This is typically done from the
master branch, except when releasing a beta (in which case the beta is released from master,
and the previous stable branch is released from said branch).If you are in a beta version,make release bump=stagewill switch to a stable.To issue an unstable version when the current version is stable, specify the
new version explicitly, likemake release bump="--new-version 4.0.0-alpha.1 devnum"
Package: wanakana
WanaKanaPythonA Python library to assist in detecting Japanese textUsageInstallpipinstallwanakanafromwanakanaimporthiraganahiragana.is_hiragana("げーむ")orfromwanakana.hiraganaimportis_hiraganais_hiragana("げーむ")DocumentationSee originalWanaKana docsas the functions are near identical.Quick Referenceis_japanese("泣き虫。！〜２￥ｚｅｎｋａｋｕ")//=>trueis_kana("あーア")//=>trueis_hiragana("げーむ")//=>trueis_katakana("ゲーム")//=>trueis_kanji("切腹")//=>trueis_romaji("Tōkyō and Ōsaka")//=>trueCreditsA partial port ofWanaKanaLicenseSource files of this project are available under the MIT License. SeeLICENSE
Package: wanakana-python
ワナカナ <--> WanaKana <--> わなかなPython utility library for detecting and transliterating Hiragana, Katakana, and RomajiThis library uses Semantic VersioningSee what this meanshereDemoVisit thewebsiteto see WanaKana in action.UsageInstallpipinstallwanakana-pythonimportwanakana# orfromwanakanaimportto_kana,is_romajiDocumentationExtended API reference(Note that function names are converted to snake_case)Quick Reference### TEXT CHECKING UTILITIES ###wanakana.is_japanese('泣き虫。！〜２￥ｚｅｎｋａｋｕ')# => Truewanakana.is_kana('あーア')# => Truewanakana.is_hiragana('すげー')# => Truewanakana.is_katakana('ゲーム')# => Truewanakana.is_kanji('切腹')# => Truewanakana.is_kanji('勢い')# => Falsewanakana.is_romaji('Tōkyō and Ōsaka')# => Truewanakana.to_kana('ONAJI buttsuuji')# => 'オナジ ぶっつうじ'wanakana.to_kana('座禅‘zazen’スタイル')# => '座禅「ざぜん」スタイル'wanakana.to_kana('batsuge-mu')# => 'ばつげーむ'wanakana.to_kana('wanakana',custom_kana_mapping={'na':'に','ka':'bana'})# => 'わにbanaに'wanakana.to_hiragana('toukyou, オオサカ')# => 'とうきょう、　おおさか'wanakana.to_hiragana('only カナ',ignore_romaji=True)# => 'only かな'wanakana.to_hiragana('wi',use_obsolete_kana=True)# => 'ゐ'wanakana.to_katakana('toukyou, おおさか')# => 'トウキョウ、　オオサカ'wanakana.to_katakana('only かな',ignore_romaji=True)# => 'only カナ'wanakana.to_katakana('wi',use_obsolete_kana=True)# => 'ヰ'wanakana.to_romaji('ひらがな　カタカナ')# => 'hiragana katakana'wanakana.to_romaji('ひらがな　カタカナ',uppercase_katakana=True)# => 'hiragana KATAKANA'wanakana.to_romaji('つじぎり',custom_romaji_mapping={'じ':'zi','つ':'tu','り':'li'})# => 'tuzigili'### EXTRA UTILITIES ###wanakana.strip_okurigana('お祝い')# => 'お祝'wanakana.strip_okurigana('踏み込む')# => '踏み込'wanakana.strip_okurigana('お腹',leading=True)# => '腹'wanakana.strip_okurigana('ふみこむ',match_kanji='踏み込む')# => 'ふみこ'wanakana.strip_okurigana('おみまい',match_kanji='お祝い',leading=True)# => 'みまい'wanakana.tokenise('ふふフフ')# => ['ふふ', 'フフ']wanakana.tokenise('hello 田中さん')# => ['hello', ' ', '田中', 'さん']wanakana.tokenise('I said 私はすごく悲しい',compact=True)# => [ 'I said ', '私はすごく悲しい']ContributorsStarwort– AuthorCreditsPort ofWanaKana
Package: wanalyzer
Whatsapp-Chat-AnalyzerAnalyzing Whatsapp Chat (Personal & Group)Current making is to analyzing the WhatsApp chat more efficientlyNext IterationNext step would be learn a model to predict the next chatAutomate my text in real timeSupported AnalysisChat CountChat AvaerageMember/Sender RankWebsite/URL/Link Domain RankWord Count and RankMost Used Word by SenderEmoji Usage RankMost Used Emoji by SenderTimestamp HeatmapAttachment Classification (In Android, there is no difference pattern for attachment. But in iOS we can actually classify between Image, Video, Audio, GIF, Sticker, Document, and Contact Card)RequirementsPython 3.6+pipinstall-rrequirements.txtUsage$ git clone https://github.com/rahuladream/Whatsapp-Chat-Analyzer.git

$ cd Whatsapp-Chat-Analyzer
$ python analyzer.py chat_example.txtPackage Installpipinstallwanalyzerusage:wanalyzerFILE[-h][-d]wanalyzer<file_path.txt>

Readandanalyzewhatsappchat

positionalarguments:FILEChatfilepath

optionalarguments:-h,--helpshowthishelpmessageandexit-d,--debugDebugmode.Showsdetailsforeveryparsedline.PreviewSender & Mentioned DomainUsed emoji and Favourite EmojiMost used word and favorite wordChat activity heatmap
Package: wanda
wandaScript to set wallpaper using keyword or randomlyInstallation / Updatepip install wanda -UOn android (with termux):
Installtermux-apipkg up
pkg in clang pkg-config libxml2 libxslt libjpeg-turbo termux-api python
pip install cython lxml==5.0.0
pip install wanda -UFor issues installing pillow refer thisdocumentUsage# Set randomly
wanda

# Set from a keyword 
wanda -t mountain

# Set from a source
wanda -s wallhaven

# Set from a source 
wanda -s wallhaven -t japan

# Set album covers
wanda -sm -t "Meltt-Love Again" -fwanda -hfor more detailsNotesBy default, the source isunsplash.Some sources may have inapt images. Use them at your own risk.Supported sources4chanviaRozen Arcana500pxartstationimgurviarimgoearthviewgeneratedlocalmusicbrainz(Album covers)picsumredditunsplashwallhavenwaifuBuildpythonandpoetryare neededgit clone https://github.com/ksh-b/wanda.git
cd wanda
poetry install
poetry build
poetry run wandaUninstallpip uninstall wandaLicenseMIT
Package: wanda-api-parameters
Wanda parametersIntroductionA Python implementation of the Excel-based parameter script.ExampleInstall the wanda-api-parameters python package.Open a command window in the folder your YAML-option file and your zipped model are stored in.Type the following code:run_yaml_file --option-file $your option file name without extension$ --model-file $your zipped model name without extension$The input options can be found by typing:run_yaml_file -h
Package: wandatoolbox
Wanda ToolboxToolbox (python scripts) for Wanda modellers. This toolbox includes several tools and utilities that can help with Wanda modelling,
running simulations and analyzing and visualizing simulation results. This module is closely related to PyWanda, the Python bindings for the Wanda API.Wanda is an advanced water hammer software, designed for engineers by engineers. The Python API bindings can be used to create Wanda models,
run simulations, and extract the simulation results. The Wanda Toolbox module includes additional scripts to go beyond the basic data retrieval of pywanda.InstallationRun the following to install this package:pip install WandaToolboxAlso see:https://pypi.org/project/wandatoolbox/UsageTutorial and reference documentation is provided atwandatoolbox.readthedocs.io. A PDF version of the manual is availablehere. And the source code is always available athttps://github.com/MichielTukker/WandaToolbox.SupportNo official support! For questions/improvements/comments, contact Deltares or Wanda support desk?
Package: wandb
Weights and BiasesUse W&B to build better models faster. Track and visualize all the pieces of your machine learning pipeline, from datasets to production models.Quickly identify model regressions. Use W&B to visualize results in real time, all in a central dashboard.Focus on the interesting ML. Spend less time manually tracking results in spreadsheets and text files.Capture dataset versions with W&B Artifacts to identify how changing data affects your resulting models.Reproduce any model, with saved code, hyperparameters, launch commands, input data, and resulting model weights.Sign up for a free account →FeaturesStore hyper-parameters used in a training runSearch, compare, and visualize training runsAnalyze system usage metrics alongside runsCollaborate with team membersReplicate historic resultsRun parameter sweepsKeep records of experiments available foreverDocumentation →QuickstartpipinstallwandbIn your training script:importwandb# Your custom arguments defined hereargs=...wandb.init(config=args,project="my-project")wandb.config["more"]="custom"deftraining_loop():whileTrue:# Do some machine learningepoch,loss,val_loss=...# Framework agnostic / custom metricswandb.log({"epoch":epoch,"loss":loss,"val_loss":val_loss})If you're already using Tensorboard orTensorboardX, you can integrate with one line:wandb.init(sync_tensorboard=True)Running your scriptRunwandb loginfrom your terminal to signup or authenticate your machine (we store your api key in ~/.netrc). You can also set theWANDB_API_KEYenvironment variable with a key from yoursettings.Run your script withpython my_script.pyand all metadata will be synced to the cloud. You will see a url in your terminal logs when your script starts and finishes. Data is staged locally in a directory namedwandbrelative to your script. If you want to test your script without syncing to the cloud you can set the environment variableWANDB_MODE=dryrun.If you are usingdockerto run your code, we provide a wrapper commandwandb dockerthat mounts your current directory, sets environment variables, and ensures the wandb library is installed. Training your models in docker gives you the ability to restore the exact code and environment with thewandb restorecommand.Web InterfaceSign up for a free account →Introduction video →Detailed UsageFramework specific and detailed usage can be found in ourdocumentation.TestingTo run basic test usemake test.  More detailed information can be found at CONTRIBUTING.md.We usecirclecifor CI.Academic ResearchersIf you'd like a free academic account for your research group,reach out to us →We make it easy to cite W&B in your published paper.Learn more →CommunityGot questions, feedback or want to join a community of ML engineers working on exciting projects?Join ourslackcommunity.Follow us onTwitter.
Package: wandbbq
WandBBQ Wandb Backup and QuitBetter APIs for good ole WandBUsageJust importwandbbqand usewandbbq.initinstead ofwandb.init.If executing an offline run, upon program terination awandb syncjob will be launched to sync your run automatically.If the env varWANDBBQ_RELAYis defined asuser@ip,wandb syncwill be executed over ssh on the provided relay (useful for computing clusters with no internet connection on compute nodes).Known LimitsThe optional relay node must be capable of accessing the same wandb run folder at the same address.No fallback if the sync node has no internet.No upload of intermediate products (will implement sometimes later).
Package: wandb-callbacks
Weights & Biases Callbackswandb-callbacksprovides some additional Callbacks for Weights & Biases.Callbacks currently implemented:ActivationCallbackvisualizes the activations of a layeractivations are computed for a sample of each classDeadReluCallbacklogs the number of dead relus in each layerprints warning if the percentage is higher than a thresholdGradCAMCallbackGrad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationproduces a coarse localization map highlighting the important regions in the image for predicting the class of the imageSample ImplementationCan be found innotebooks/sample_implementation.ipynb.
Package: wandb-core
wandb-core: A New Backend for the W&B SDKIntroductionGood News, Everyone! We've developed a new and improved backend for the W&B SDK that is
more performant, versatile, and robust.Getting StartedTo start usingwandb-core, simply install
the package in your environment. Thewandblibrary will automatically detect and utilize it:pipinstall-Uwandbwandb-coreNote: ensure you havewandb>=0.16.1.Platform Compatibilitywandb-corecomes with wheels pre-built for the following platforms:Linux:x86_64,aarch64macOS:x86_64,arm64Windowsamd64For other platforms, buildwandb-corefrom the source as outlined in ourcontributing guide. If you're interested in support for additional platforms, please inform us by opening aGitHub issue. Your feedback helps us prioritize new platform support.W&B Server Compatibilitywandb-coreis compatible with our production and dedicated cloud deployments as well asW&B Serverversions>=0.40.0.Switching Back to the Old SDK BackendTo revert to the old SDK backend, simply uninstallwandb-corefrom your environment:pipuninstallwandb-coreContributingYour contributions are welcome! Check ourcontributing guidefor instructions on setting up your development environment and contributing to the project.Feedback and Bug ReportingWe're eager to hear your thoughts onwandb-core. Your feedback, especially bug reports, is invaluable. If you encounter any issues, please raise aGitHub issueand mention your use ofwandb-core.Feature Support StatusBelow is an overview of the feature support status in thewandb-coreversion0.17.0b9.Status legend:✅: Available: The feature is relatively stable and ready for use.🚧: In Development: The feature is available but may be unstable or incomplete.❌: Not Available: The feature is not yet available.CategoryFeatureStatusExperimentsinit✅[^E.1]log✅log_artifact✅log_code✅config✅summary✅define_metric🚧[^E.5]tags✅notes✅name✅alert✅save✅restore✅mark_preempting✅resume✅reinit✅Media✅Grouping✅anonymous mode✅offline mode✅disabled mode✅multiprocessing✅TensorBoard sync🚧[^E.1]console logging✅[^E.8]system metrics✅[^E.9]system info✅auto code saving✅Settings🚧[^E.12]Logindefault entity✅team entity✅service account🚧CLIsync✅[^E.1][^CLI.1]<other commands>🚧[^CLI.2]Artifacts✅Sweeps✅Launch✅Sweeps on Launch🚧[^L.1][^E.1]:sync_tensorboardonly uploadstfeventfiles to W&B, but
metrics will not be logged in native W&B charts.
[^E.5]:define_metriconly supports default summary.
[^E.8]: Only raw console logging is supported.
[^E.9]: Supported system metrics: CPU, Memory, Disk, Network, NVIDIA GPU, AMD GPU, Apple GPU.
[^E.12]: Unsupported settings:
(anonymous,_flow_control*,_stats_open_metrics_endpoints, ...)
[^CLI.1]: The command is namespaced underwandb betagroup.
[^CLI.2]: The rest of the CLI works, but uses the old backend under the hood for some
commands.
[^L.1]: Sweeps on Launch requires verification.
Package: wandbfs
Wandb fsspec implementationWandbis a machine learning tool which allows you to tracks your experiments. While the experiment is running, it tracks its metrics and helps you visualize the progress. You can also upload files such as configuration files, models, datasets (some files are uploaded automatically). After the experiment is finished, you can access the experiment's files (only read-only). This implementation allows researchers who use wandb to access the finished experiment's files using thefsspec interface.Getting startedInstall the package by running:pip install wandbfsThen, you can open any file from your wandb runs as follows:import fsspec

f = fsspec.open('wandb://{entity}/{project}/{run_name_or_run_id}/{path}')You can also list the files and subdirectories by using thelscommand.import fsspec

fs = fsspec.filesystem('wandb')

files = fs.ls('{entity}/{project}/{run_name_or_run_id}/{path}')Alternatively, you can list projects or runs:import fsspec

fs = fsspec.filesystem('wandb')

projects = fs.ls('{entity}')
runs = fs.ls('{entity}/{project}')
Package: wandbmon
WandMonWandMon is a lightweight Python library designed to help you seamlessly monitor the inputs and outputs of your machine learning model in a production environment. Developed by Weights and Biases, it offers the capability to stream the monitoring data directly to your Weights and Biases dashboard.Wrap any prediction function with a@monitordecorator and WandMon provides real-time tracking of your function calls with minimal interference to your existing pipeline.Key FeaturesReal-time monitoring: WandMon captures real-time system utilization, call time, and number of calls.Flexibility: Easily specify an input preprocessor or output postprocessor to have more control over what gets logged.Direct logging to Weights and Biases: By default, WandMon logs all keyword arguments.Dashboard integration: WandMon is fully compatible with Weights and Biases' workspaces and integrates withWeaveenabling easy analysis and customization.InstallationYou can install WandMon via pip:pip install -e .Getting StartedTo get started with WandMon, simply import the library and use the@monitordecorator for your function. WandMon will automatically log the inputs and outputs of your function to your Weights and Biases account.Seeexamplesfor some getting started examples.fromwandmonimportmonitor@monitordefmy_function(a,b,c):...Once you've set up WandMon, callingmy_functionwill automatically stream its inputs and outputs to Weights and Biases:my_function(1,2,3)For more control over what data is logged, you can provide an input_preprocessor or output_postprocessor:defmy_input_preprocessor(kwargs):return{key:valueforkey,valueinkwargs.items()ifkeyin["a","b"]}defmy_output_postprocessor(result):return{"result":result}@monitor(input_preprocessor=my_input_preprocessor,output_postprocessor=my_output_postprocessor)defmy_function(a,b,c):...Weights and Biases IntegrationTo view the logged data, visit your Weights and Biases dashboard. The dashboard displays real-time system utilization, call time, and inputs and outputs.WandMon makes it easier to monitor your production ML models and visualize data to get insights.Feedback and ContributionWe would love to hear your feedback and are open to contributions. Please feel free to open an issue or send a pull request.Developmentpipinstall-rrequirements.test.txtLicenseWandMon is under theApache 2.0.Whether you're tracking your models' performance over time, comparing different models, or trying to diagnose the cause of a recent crash, WandMon and Weights and Biases have you covered. Let's get your models monitored, understood, and improved!
Package: wandb-ng
Weights and BiasesUse W&B to organize and analyze machine learning experiments. It's framework-agnostic and lighter than TensorBoard. Each time you run a script instrumented withwandb, we save your hyperparameters and output metrics. Visualize models over the course of training, and compare versions of your models easily. We also automatically track the state of your code, system metrics, and configuration parameters.Sign up for a free account →FeaturesStore hyper-parameters used in a training runSearch, compare, and visualize training runsAnalyze system usage metrics alongside runsCollaborate with team membersReplicate historic resultsRun parameter sweepsKeep records of experiments available foreverDocumentation →QuickstartpipinstallwandbIn your training script:importwandb# Your custom arguments defined hereargs=...wandb.init(config=args,project="my-project")wandb.config["more"]="custom"deftraining_loop():whileTrue:# Do some machine learningepoch,loss,val_loss=...# Framework agnostic / custom metricswandb.log({"epoch":epoch,"loss":loss,"val_loss":val_loss})If you're already using Tensorboard orTensorboardX, you can integrate with one line:wandb.init(sync_tensorboard=True)Running your scriptRunwandb loginfrom your terminal to signup or authenticate your machine (we store your api key in ~/.netrc). You can also set theWANDB_API_KEYenvironment variable with a key from yoursettings.Run your script withpython my_script.pyand all metadata will be synced to the cloud. You will see a url in your terminal logs when your script starts and finishes. Data is staged locally in a directory namedwandbrelative to your script. If you want to test your script without syncing to the cloud you can set the environment variableWANDB_MODE=dryrun.If you are usingdockerto run your code, we provide a wrapper commandwandb dockerthat mounts your current directory, sets environment variables, and ensures the wandb library is installed. Training your models in docker gives you the ability to restore the exact code and environment with thewandb restorecommand.Web InterfaceSign up for a free account →Introduction video →Detailed UsageFramework specific and detailed usage can be found in ourdocumentation.TestingTo run basic test usemake test.  More detailed information can be found at CONTRIBUTING.md.We usecirclecifor CI.Academic ResearchersIf you'd like a free academic account for your research group,reach out to us →We make it easy to cite W&B in your published paper.Learn more →CommunityGot questions, feedback or want to join a community of ML engineers working on exciting projects?Join ourslackcommunity.Follow us onTwitter.