Saravanan666/pypi-org huggingface dataset:
Package: wolkenbrot
Wolkenbrotbakes and manages your AWS cloud imageswolkenbrot is named after a German children's title called Wolkenbrot by
the Korean authors Baek Hee Na Kim Hyang Soo. The translation to English is
cloud's bread.Wolken brot is inspired by packer1and kujenga2, removing fabric as a
dependency. It also aims to be more tested and documented.In case you wonder, yes it's similar to packer by Hashicorp.
But here are some reasons that you might like it better than packer:It's written in Python.It's not written in Go.It can use private AMI as a starting point for your build.It's smaller and easier to hack on.It has a cooler name.It has a better license, MIT sounds way better then MPL-2. Especially because it means "with" in German.how is it different from Packer or kujenga?It is Python3 only.It does not depend on fabric.It replaces boto with boto3It's name is not Swahili, but rather German.It adds the ability to list your imagesIt adds the ability to get detailed information about an image from the CLI.It adds the ability to de-register images from the CLI.USAGEYou can run the following command to build an image:$ wolkenbrot bake <image.json>See the documentation for the JSON format.You can run the following command to list your existing images:$ wolkenbrot listYou can view the information about and image:$  wolkenbrot info ami-72192eYou can remove the image with:$ wolkenbrot delete ami-72192eWolkenbrot follows boto3 configuration principles, so if you wonder how to
pass AWS configuration parameters, take a look inBoto3's own documentationFAQDo you support Windows or Mac OS X?The author of this software strongly despises working on Windows or
Mac OS X. Hence, this software is not tested for these platforms.
If you can run Python on your OS, it might run.Do you support provisioning machines with Saltstack\Chef\Puppet\Ansible\XYZ?Yes, just install them via shell first, then call the right binary with the correct playbook\state\formula..Testing and Installing the test requirementsSimply issue:$ pip install -e ".[dev]"
$ make test
Package: wolne-lektury
wolne_lekturyAn unofficial REST API client forWolne LekturyInstallationYou can install this library fromPyPIpipinstallwolne_lekturyor from the GitHub repo:pipinstallgit+https://github.com/krzjoa/wolne_lektury.gitUsageimportwolne_lekturyaswl# Query is automatically slugified, so you can type author names in natural languagewl.get_books(authors="Juliusz Słowacki")wl.get_books(authors="adam-mickiewicz")# Use more complex queries. You can specify language as wellwl.get_books(epoch="Romantyzm",genre="Powieść")wl.get_books(epoch="Modernizm",kind="Liryka",language="pol")# Get lists of authors, epochs, genres, kinds, themes and collenctionswl.get_authors()wl.get_epochs()wl.get_genres()wl.get_kinds()wl.get_themes()wl.get_collections()# Retrieve full textes or download them as filesbooks=wl.get_texts(author="Ignacy Krasicki")print(list(books.values())[4][:60])'Dwa żółwie\r\n\r\n\r\n\r\nNie żałując sił własnych i ciężkiej fatygi'wl.download("output_dir",author="Henryk Sienkiewicz",book_format=wl.BookFormat.PDF)
Package: wolof
Library, built on PyTorch and Transformers, for developing state-of-the-art deep learning models on a wide variety of linguistic tasks in wolof.Your Library for Wolof LanguageWolofis a language spoken in Senegal in neighboring countries, many works are written in Wolof or the need to have a tool that allows us to know better this language.Wolof libraryallows us to do several specific tasks in Wolof languages such as text classification, translation, automatic speech recognition.Why Wolof library ?:simple and easy to usecustomizableclean codeInstallationRequirementsPython >= 3.6TorchTransformersWith pipwolof can be installed using pip as follows:pip install wolofFrom sourcepipinstallgit+https://github.com/abdouaziz/wolof.gitUsagefromwolof.asrimportSpeech2Textasr=Speech2Text(model_name="abdouaziiz/wav2vec2-xls-r-300m-wolof")audio_file="audio.wav"prediction=asr.predict(audio_file)PipelineThe pipelines are a great and easy way to use models for inference. These pipelines are objects that abstract most of the complex code from the library,offering a simple API dedicated to several tasks Masked Language Modeling, Sentiment Analysis .bert-base-wolofis pretrained bert-base model on wolof language  .sora-wolofis pretrained roberta model on wolof language  .Models in Wolof libraryModel nameNumber of layersAttention HeadsEmbedding DimensionTotal Parametersbert-base-wolof61251456931622 Msoraberta-base61251483 MUsing Soraberta or BERT-base-wolof>>>fromwolofimportPipeline>>>unmasker=Pipeline(task='fill-mask',model_name='abdouaziiz/bert-base-wolof')>>>unmasker("kuy yoot du [MASK].")[{'sequence':'[CLS] kuy yoot du seqet. [SEP]','score':0.09505125880241394,'token':13578},{'sequence':'[CLS] kuy yoot du daw. [SEP]','score':0.08882280439138412,'token':679},{'sequence':'[CLS] kuy yoot du yoot. [SEP]','score':0.057790059596300125,'token':5117},{'sequence':'[CLS] kuy yoot du seqat. [SEP]','score':0.05671025067567825,'token':4992},{'sequence':'[CLS] kuy yoot du yaqu. [SEP]','score':0.0469999685883522,'token':1735}]fortaskwe can have the following values: 'fill-mask', 'sentiment-analysis'You can checkout examples inexamples/AuthorAbdou Aziz DIOP @abdouazizemail :abdouaziz.g@gmail.comlinkedin :https://www.linkedin.com/in/abdouaziiz/
Package: wolpert
Wolpert, a stacked generalization frameworkWolpert is ascikit-learncompatible framework for easily building stacked ensembles. It supports:Different stacking strategiesMulti-layer modelsDifferent weights for each transformerEasy to make it distributedQuickstartInstallThe easiest way to install is using pip. Just runpip install wolpertand you’re ready to go.Building a simple modelFirst we need the layers of our model. The simplest way is using the helper functionmake_stack_layer:from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from wolpert import make_stack_layer, StackingPipeline

layer0 = make_stack_layer(SVC(), KNeighborsClassifier(),
                          RandomForestClassifier(),
                          blending_wrapper='holdout')

clf = StackingPipeline([('l0', layer0),
                        ('l1', LogisticRegression())])And that’s it! AndStackingPipelineinherits a scikit learn class: thePipeline, so it works just the same:clf.fit(Xtrain, ytrain)
ypreds = clf.predict_proba(Xtest)This is just the basic example, but there are several ways of building a stacked ensemble with this framework. Make sure to check theUser Guideto know more.
Package: wolproxypycli
wolproxypycliA PyPI module and Typer (CLI) app for sending Wake-On-LAN packetsInstallationThis project usesPoetryfor dependency management. To install the project, first installpoetry, then run the following command in the project root directory:poetryinstallormakeinstallDevelopment installationTo install the package with all development dependencies, run:makeinstall-devUsageWake on lan must be enabled on the target host device before usage.For more details, check thedocumentation.Command lineThis tool is typically used to send a Wake-on-LAN packet to a device on your network. The<MAC>argument should be replaced with the MAC address of the device you want to wake up.Here's an example:poetryrunwolproxypycli00:11:22:33:44:55To see the full list of options, run:poetryrunwolproxypycli--help╭─ Arguments ──────────────────────────────────────────────────────╮
│ * mac                 TEXT     [default: None] [required]        │
╰──────────────────────────────────────────────────────────────────╯
╭─ Options ────────────────────────────────────────────────────────╮
│ --ip                  TEXT     [default: 255.255.255.255]        │
│ --port                INTEGER  [default: 9]                      │
│ --interface           TEXT     [default: None]                   │
│ --install-completion  Install completion for the current shell.  │
│ --show-completion     Show completion for the current shell.     │
│ --help                Show this message and exit.                │
╰──────────────────────────────────────────────────────────────────╯ModuleFirst, make sure to install the module viapoetry:poetryaddwolproxypycliorpip:pipinstallwolproxypycliHere's a basic example of how to usewolproxypycli:fromwolproxypycliimportwol...wol.send(mac="AA:BB:CC:DD:EE:FF")LicenseThis project is licensed under the terms of theMIT License.
Package: wol-redirect-proxy
Wake-on-LAN redirect proxyBrutally simple, single file, Fast API based proxy service. Create a simplepathand/orhostnamebased
redirects that send WoL packets if target URL is not reachable. Only works forHTTP(S)redirects.Great complement for a simpleauto-suspendsetup,
e.g. your home jellyfin server or any otherHTTPservice.That's it!Install# 1. install script - install wol-proxy into a new virtualenv inside your $HOMEcurlhttps://raw.githubusercontent.com/xyzjonas/wol-redirect-proxy/main/install.sh|bash# 2. from PyPipipinstallwol-redirect-proxy# 3. from source using Poetrypoetryinstall...and runwol-proxy--host"0.0.0.0"--port12345wol-proxy--helpConfigurationSpecify a list of proxy mappings using a yaml config, e.g.:#  This will WoL redirect any HTTP request and carry over the path.#  e.g.: POST: http://my-proxy.home/login -> POST "http://my-jellyfin.home:8096/login"targets:-handler:"wol"source_url:"http://my-proxy.home/*"target_url:"http://my-jellyfin.home:8096"methods:[GET,POST,DELETE,PATCH]options:mac:"75:55:39:a4:33:27"timeout_s:2#  Multiple redirects to different services by hostname#  (requires working DNS setup)targets:-handler:"plain"source_url:"http://jellyfin.home/*"target_url:"http://192.168.0.124:8096"methods:[GET,POST,DELETE,PATCH]-handler:"plain"source_url:"http://nextcloud.home/*"target_url:"http://192.168.0.129:8080"methods:[GET,POST]-handler:"plain"source_url:"http://torrent-box.home/*"target_url:"http://192.168.0.135:8080"methods:[GET,POST]Use as a systemd service# use install script and follow instructionscurlhttps://raw.githubusercontent.com/xyzjonas/wol-redirect-proxy/main/install.sh|bash# use the sample systemd unit filegitclonehttps://github.com/jonasbrauer/wol-redirect-proxy.git&&cdwol-redirect-proxysudocpsample-systemd.unit/etc/systemd/system/wol-redirect-proxy.service# ...and edit user/groupsudosystemctldaemon-reload
sudosystemctlenable--nowwol-redirect-proxy.service
sudosystemctlstatuswol-redirect-proxy.service
Package: wolsey
Utilities for CromwellUsage# Build the virtualenv and install required modules,
# including this one in editable mode
# Python 3
0/make_venv3.sh
# Python 2
0/make_venv2.sh. venv/bin/activate# Build the module
0/build.sh# Register the built module with PyPI
0/register.sh# Upload a new build to PyPI
0/upload.sh# Delete all built files
0/clean.shDevelopmentRun0/make_venv3.shor0/make_venv2.shfirst. venv/bin/activate
# This IPython config will set autoreload
ipython –config=0/ipython_config.py
Package: wolvr
Utility Library Module for Advanced Machine LearningInstallpipinstallwolvrExample 1: Naive Bayesfromwolvrimportnaive_bayesasnbnb.demo(dataset="SMS_spam")Selected Output:This demo uses a public dataset of SMS spam, which has a total of 5574 messages = 747 spam and 4827 ham (legitimate).
The goal is to use 'term frequency in message' to predict whether the message is ham (class=0) or spam (class=1).

Using a grid search and a multinomial naive bayes classifier, the best hyperparameters were found as following:
   Step1: Tokenizing text: CountVectorizer(analyzer = 'word', ngram_range = (1, 1));
   Step2: Transforming from occurrences to frequency: TfidfTransformer(use_idf = True).

The top 2 terms with highest probability of a message being a spam (the classification is either spam or ham):
   "claim": 80.73%
   "prize": 80.06%

Application example:
   - Message: "URGENT! We are trying to contact U. Todays draw shows that you have won a 2000 prize GUARANTEED. Call 090 5809 4507 from a landline. Claim 3030. Valid 12hrs only."
   - Probability of class=1 (spam): 98.32%
   - Classification: spamExample 2: k-Nearest NeighborsfromwolvrimportkNNkNN.demo("Social_Network_Ads")Selected Output:This demo uses a public dataset of Social Network Ads, which is used to determine what audience a car company should target in its ads in order to sell a SUV on a social network website.

Using a grid search and a kNN classifier, the best hyperparameters were found as following:
   Step1: scaler: StandardScaler(with_mean=True, with_std=True);
   Step2: classifier: kNN_classifier(n_neighbors=8, weights='uniform', p=1.189207115002721, metric='minkowski').Example 3: Decision Boundary ComparisonfromwolvrimportkNNkNN.demo("Social_Network_Ads")fromwolvrimportnaive_bayesasnbnb.demo("Social_Network_Ads")fromwolvrimportSVMSVM.demo("Social_Network_Ads")module: model_evaluationfunctiondescriptionplot_confusion_matrix()plots the confusion matrix, along with key statistics, and returns accuracyplot_ROC_curve()plots the ROC (Receiver Operating Characteristic) curve, along with statisticsplot_PR_curve()plots the precision-recall curve, along with statisticsplot_ROC_and_PR_curves()plots both the ROC and the precision-recall curves, along with statisticsdemo()provides a demo of the major functions in this modulemodule: naive_bayesfunctiondescriptionnaive_bayes_Bernoulli()when X are independent binary variables (e.g., whether a word occurs in a document or not)naive_bayes_multinomial()when X are independent discrete variables with 3+ levels (e.g., term frequency in the document)naive_bayes_Gaussian()when X are continuous variablesdemo()provides a demo of selected functions in this modulemodule: kNNfunctiondescriptiondemo()provides a demo of selected functions in this modulemodule: neural_networkfunctiondescriptionrnn()Recurrent neural networkdemo()provides a demo of selected functions in this modulemodule: decision_treefunctiondescriptionboost()Boostingdemo()provides a demo of selected functions in this module
Package: womack
Womack is a service that you can use to push realtime events between
your regular, plain-old, non-websockety web application and
clients. It is built on top ofgevent-socketioandredis.Why Womack?You want to write an application, like a game or shared calendar or
group chat or some kind of game that involves chatting about calendars
in a group. You want the convenience of writing most of your
application in the regular, stateless, blocking way you are used to
writing web applications, and access to all of the nice tools that you
have over in that box. But you also want your calendar game UI to be
snappy and realtime and not rely on polling or keeping track of game
state in two places. So you put a service like Womack in the
middle and use it to push real-time events to clients as your
blocking application receives input and does its thing in the
database or S3 or wherever.Quickstart:Install and startredisClone this repository.Make avirtualenv, then install dependencies withmake serverNoteOn Windows you may need to install gevent manually.Install Womack:python setup.py developStart womack server:womackIn a browser, loadhttp://localhost:8111/test.htmlIn a python shell, enter:>>> import womack.publish
>>> wm = womack.publish.Publisher()
>>> wm.publish('hello', {'hello': 'world'})You should see the message“hello world”appear immediately in
the browser.Similar projectsWomack was heavily inspired by and includes some client-side code
derived from the excellent nodejs projectjuggernautand thepython clientfor juggernaut.Womack has slightly different internal message routing from
juggernaut, making it (in our opinion at least) more appropriate for
multi-tenant applications. And it is also easier for python
programmers to customize, since it is written in python.About the nameYou don’t know what you’ll do until you’re put under pressure…
Package: wombatoo
WOMBATOOWombatoo is a python library which have the following features:Dataframe summary: Summary of dataframe in CSV formatDatacard: A single document having Timestamp, data owner, data source, data refresh cycle, data details, Intended use, data summary, ethical considerations, Recommendation and caveats. Some fields are pre-populated and the user can populate the rest to define a consice contract between data owner and data user.Version: a local versioning tool using which a user can use to version small to medium datasets locally.Report: A pandas-profiling report which can be saved in HTML, JSON and PDF formats.Schema: A csv document having the schema of the dataset. Users can use it to validate the schema in the data pipeline.Installationpip install wombatoofrom wombatoo import wombatoo
import pandas as pd

#reading df
df = pd.read_csv('base.csv')

if __name__ =="__main__":
    summary = wombatoo.metric_summary(df,path_to_save='summary.csv')
    datacard = wombatoo.datacard(df, path_to_save='Datacard_user.txt')
    version = wombatoo.version(df.head(5),file_name='fraud_bool', folder_path='./local_repo')
    report = wombatoo.generate_report(df=df.head(10), path_to_save='./Data_Profile_Report.html', pdf=False)
    schema = wombatoo.schema(df=df,path_to_save='schema.csv',show=1)END
Package: wombo
Hi there, I'mI am a module for using wombo dream ai (neural network of image generation)There are some changes in the new version of dream, We did not record the last version, so you will have to rewrite your previous code. We apologize for the sincere inconvenienceMini DocumentationSynchro versionImportBasic importfrom wombo import Dream # sync
dream = Dream(out_msg: str,  max_requests_per_token: int = 30)
# out_msg: Message for response errors from the serverGet stylesdream.get_styles() -> StyleModel
# return pydantic modelCheck taskdream.check_task(self, task_id: str, only_bool: bool = False) -> Union[CheckTask, bool]
# only_bool: If this flag is set, false or True will be returned, depending on the completeness of the taskCreate taskdream.create_task(self, text: str, style: Union[Style, int] = Style.BASESTYLE) -> CreateTask
# style: Style identifier, yes, too fashionable class, it's on styleGeneratedream.generate(text: str, style: Style = Style.BASESTYLE, timeout: int = 60, check_for: int = 3, gif: bool = False)  -> Union[io.BytesIO, CheckTask]
# style: Style identifier, yes, too fashionable class, it's on style
# timeout: The number of seconds after which the task will stop being checked and will throw Timeout error
# check_for: Race in how many seconds to do the check
# gif: Flag, create a gif or notGIFdream.gif(self, url_list: list) -> io.BytesIO
# url_list: List[str] list of links to images (CheckTask.photo_url_list)Asynchronous versionImportBasic importfrom wombo import AsyncDream # sync
dream = AsyncDream(out_msg: str,  max_requests_per_token: int = 30)
# out_msg: Message for response errors from the serverGet stylesawait dream.get_styles() -> StyleModel
# return pydantic modelCheck taskawait dream.check_task(self, task_id: str, only_bool: bool = False) -> Union[CheckTask, bool]
# only_bool: If this flag is set, false or True will be returned, depending on the completeness of the taskCreate taskawait dream.create_task(self, text: str, style: Union[Style, int] = Style.BASESTYLE) -> CreateTask
# style: Style identifier, yes, too fashionable class, it's on styleGenerateawait dream.generate(text: str, style: Style = Style.BASESTYLE, timeout: int = 60, check_for: int = 3, gif: bool = False)  -> Union[io.BytesIO, CheckTask]
# style: Style identifier, yes, too fashionable class, it's on style
# timeout: The number of seconds after which the task will stop being checked and will throw Timeout error
# check_for: Race in how many seconds to do the check
# gif: Flag, create a gif or notGIFawait dream.gif(self, url_list: list, thread: bool = True) -> io.BytesIO
# url_list: List[str] list of links to images (CheckTask.photo_url_list)
# thread: Run the task to create a gif in another thread, so as not to block the programStyleStyle().get_awaible_styles: Getting available styles
    Style.BASESTYLE: Basic style
    Style().{name_style}: Specifying a style, works only after initialization, does not update styles automaticallyCreditless@mayneryther give me algoritm@pokedim13meRequirementshttpxpydanticpillowOptinal
Package: wombopy
wombopywombopyis a commands-line program to generate and download images fromwombo.art. It requires Python interpreter, version 3.7+.Please,starthis project to support it.Installationpip install wombopyDocumentationSee this issue to#2to start using wombopy.And this issue to#1to know how to get an identify_key.wombopy --helpUsage: wombopy [OPTIONS] COMMAND [ARGS]...

Options:
  -v, --version         Show the application's version and exit.
  --install-completion  Install completion for the current shell.
  --show-completion     Show completion for the current shell, to copy it or
                        customize the installation.
  --help                Show this message and exit.

Commands:
  run  Create an image from wombo.artwombopy run --helpUsage: wombopy run [OPTIONS] IDENTIFY_KEY PROMPT STYLE

  Create an image from wombo.art

Arguments:
  IDENTIFY_KEY  [required]
  PROMPT        [required]
  STYLE         [required]

Options:
  -s, --show  Show back the image
  --help      Show this message and exit.
Package: wommit
WommitWrite committoolA package for intuitively formatting appealing commmit messages with emojis, using an assortment of different methods.Installation:pip install wommitUsage:Autocomplete modeMenu modeTabwill call the autocompleters, althoughcomplete while typingis activated.Startingautocomplete modeby hittingTabwill bring up thetypesadded to the project.Adding(after the feature inautocomplete modewill autocomplete the scopes added to the project.#in the body of either mode will autocomplete active issues and PRs.(in the body of either mode will autocomplete recent commits.Answering yes to the Breaking Change prompt inmenu modeor writingBREAKING CHANGEin theautocomplete modewill
mark the commit as a breaking change.Autocomplete modewill attempt to autocompleteBREAKING CHANGE:if it's on a clean line.Bothmodeswill only terminate if their input is correctly formatted.Commandswommit ...:c: Commit all staged files using an intuitive selection menu or a fast autocompletion prompt.Options:-m: Use menu mode, overriding default./-e: Use autocompletion mode, overriding default.-a: Add all files to commit. (git add .)-m [MESSAGE]: Write a manual commit message, and commits if it's in the accepted format, as well as converting known types to emojis.-g: Use global settings and data instead of local.--test:  Test either of the modes without commiting.check: Manually check previously added commit.Options:-id [HASH]: Check a commit message with the specified ID.-ids [HASH1] [HASH2]: Check all commit messages between two IDs (newest ID first).-m [MESSAGE]:  Check if the given string passes the check.-l:  Check all local commits that have not been pushed.configure ...: Opens a prompt for adding/removing types and scopes.e: Edit current types and scopes.p: Prints all types and scopes.s: Edit settings.Options:-g: Edit global settings.-t: Manually test the functionality.changelog: Prints out a changelog generated from commits since last release.Options:-r: Prints a changelog designed for Github releases.-b: Prints an easily readable changelog.-e: EXPERIMENTAL: edit the latest Github release with the generated changelog.post: Checks commits between two latest releases instead of latest to now.format: Pastes the format a message needs to meet in order to pass the check.InfoUse thewommit-changelog-actionin your wommit project to automatically release your project with appropriate changelogs.