Saravanan666/pypi-org huggingface dataset:
Package: silence-lint-error
silence-lint-errorSilent linting errors by addingignoreorfixmecomments.This tool currently works with:fixitflake8(silence only)ruffUsageInstall with pip:python-mpipinstallsilence-lint-errorYou must also install the linting tool you wish to usein the same virtual
environment.silence linting errorsFind linting errors and add theignoreorfixmecomments as applicable.For example, to addlint-fixme: CollapseIsinstanceCheckscomments to ignore
thefixit.rules:CollapseIsinstanceChecksrule fromfixit, run:silence-lint-errorfixitfixit.rules:CollapseIsinstanceCheckspath/to/files/path/to/more/files/To addnoqa: F401comments to ignore theF401rule inflake8, run:silence-lint-errorflake8F401path/to/files/path/to/more/files/To addnoqa: F401comments to ignore theF401rule inruff, run:silence-lint-errorruffF401path/to/files/path/to/more/files/fix silenced errorsIf there is an auto-fix for a linting error, you can remove theignoreorfixmecomments and apply the auto-fix.For example, to remove alllint-fixme: CollapseIsinstanceCheckscomments and
apply the auto-fix for that rule, run:fix-silenced-errorfixitfixit.rules:CollapseIsinstanceCheckspath/to/files/path/to/more/files/To removenoqa: F401comments and apply the auto-fix for that rule, run:fix-silenced-errorruffF401path/to/files/path/to/more/files/RationaleWhen adding a new rule (or enabling more rules) for a linter on a large
code-base, fixing the existing violations can be too large a task to do quickly.
However, starting to check the rule sooner will prevent new violations from
being introduced.Ignoring existing violations is a quick way to allow new rules to be enabled.
You can then burn down those existing violations over time.This tool makes it easy to find and ignore all current violations of a rule so
that it can be enabled.
Package: silenceofthelambs
silenceofthelambsA python module that decryptsshush-like
KMS-encrypted strings stored in the environment, for use in places where usingshush envas your entrypoint is not an option, e.g. AWS Lambda.Usagesilenceofthelambshas two key usages:Imported and manually activatedimportsilenceofthelambs# in your main() func, or wherever makes sensesilenceofthelambs.decrypt()If you want to provide your own boto3 KMS client (e.g. if you need to use different credentials/region):importsilenceofthelambsimportboto3myclient=boto3.client("kms",.....)silenceofthelambs.decrypt(kms_client=myclient)Imported and automatically activatedsilenceofthelambsalso supports auto-activation, using a default boto3 KMS client.importsilenceofthelambs.auto
Package: silence-tensorflow
Simple python package to shut up Tensorflow warnings and logs, letting you focus on the
important errors.How do I install this package?As usual, just download it using pip:pipinstallsilence_tensorflowHow do I use it?You only need to import the package before import Tensorflow:fromsilence_tensorflowimportsilence_tensorflowsilence_tensorflow()importtensorflowastf...# your codeCan it be done within the import?Sure, you can do everything with a single line by
importing the submodule auto:importsilence_tensorflow.autoimporttensorflowastf...# your codeHow can I get pylint to ignore the unused import?You can use the flag ‘disable=unused-import’ as such:importsilence_tensorflow.auto# pylint: disable=unused-importimporttensorflowastf...# your codeHow can I get pylint to ignore the unused variable?If you import silence_tensorflow in the context of a function
you will get a different warning from pyling: unused variable.
You can use the flag ‘disable=unused-variable’ as such:deffunc():importsilence_tensorflow.auto# pylint: disable=unused-variableimporttensorflowastf...# your codeHow does this work under the hood?This package will set theKMP_AFFINITYsystem variable to“noverbose”,TF_CPP_MIN_LOG_LEVELto level3(only errors logged).If you need a custom value forKMP_AFFINITYyou should reset it after importing the package, as follows:importosfromsilence_tensorflowimportsilence_tensorflowbackup=os.environ["KMP_AFFINITY"]silence_tensorflow()os.environ["KMP_AFFINITY"]=backup
Package: silene
SileneSilene is an open source web crawler framework built uponPyppeteer.RequirementsYou must have at leastPython 3.7installed.InstallationTo install the latest release runpip install silene.Quickstart guideEach crawler must subclass theCrawlerclass and implement the abstractconfiguremethod. TheCrawlerConfigurationspecifies the initial requests to make and other properties of the crawler. Once a request is processed, the appropriate
callback will be invoked. By default, in case of a successful request theon_response_successcallback will be executed. This is where you can interact with the page content. You can also
specify custom callbacks for your requests.Below you can find a very simple implementation.Example code snippetfromsilene.crawl_requestimportCrawlRequestfromsilene.crawl_responseimportCrawlResponsefromsilene.crawlerimportCrawlerfromsilene.crawler_configurationimportCrawlerConfigurationclassMyCrawler(Crawler):defconfigure(self)->CrawlerConfiguration:returnCrawlerConfiguration([CrawlRequest('https://example.com')])defon_response_success(self,response:CrawlResponse)->None:# Do something with the response...passDevelopment instructionsPrerequisiteThis project requiresPipenvto be installed.Create environmentRunpipenv install --devto create a new virtual environment and install the necessary packages.Run testsRunpytestin the project root folder.Run tests with coverageRunpytest --cov=silenein the project root folder.LicenseThe source code of Silene is made available under
theApache License, Version 2.0.
Package: silentor-cli
silentor-cli------------[![PyPI version](https://badge.fury.io/py/silentor-cli.svg)](http://badge.fury.io/py/silentor-cli)command line interface for [silentor](http://github.com/jayin/silentor)install-------```shellpip install silentor-cli```Usage------ view the latest version of silentor```shell$ silentor version```- create a silentor application```shell$ silentor new {you app name}```- run you application```shell$ cd path/to/{you app name}$ silentor server {port}```OK,just checkout you browserDevelopment------------ upgrade the lasest release of silentorautomatic```shell$ ./upgrade.py```or manually```shell$ cd path/to/silentor-cli$ git checkout -q gh-pages$ git pull$ git checkout {tag_name}```TODO----- [ ] auto upgrade your silentor appLicense-------MIT
Package: silentwav
Creates a silent WAV file with the specified duration and saves it to the given filename.pip install silentwavTested against Windows 10 / Python 3.10 / AnacondaCreatesasilentWAVfilewiththespecifieddurationandsavesittothegivenfilename.Parameters:filename(str):ThepathandfilenameoftheWAVfiletobecreated.duration(int|float):ThedurationofthesilentWAVfileinseconds.sample_rate(int,optional):Thenumberofsamplespersecond.Defaultsto44100.num_channels(int,optional):Thenumberofaudiochannels.Defaultsto1(mono).sample_width(int,optional):Thesizeofeachaudiosampleinbytes.Defaultsto2bytes(16-bitaudio).compression_type(str,optional):ThetypeofcompressionfortheWAVfile.Defaultsto"NONE".compression_name(str,optional):Adescriptionofthecompressionused.Defaultsto"not compressed".Returns:None:ThisfunctiondoesnotreturnanyvaluebutcreatesasilentWAVfileandsavesittothespecifiedfilename.Example:fromsilentwavimportcreate_silent_wav# Creates a 60-second silent WAV file named "c:\\silent_file.wav"create_silent_wav("c:\\silent_file.wav",duration=60)
Package: silero-advanced
👋 Приветствуем в Silero-Advanced!Silero-Advanced - это улучшенный модуль Silero.🤔 В чем отличие?Оригинальная Silero не может озвучивать цифры и некоторые символы. В этом модуле данная проблема решена.⬇️ Установкаpipinstallsilero-advanced💻 ИспользованиеМодуль имеет 3 функции:• Синтезировать• Синтезировать и воспризвести• Синтезировать и сохранить аудио в формате wavПример 1 функции:importtimeimportsounddeviceassdfromsilero_advancedimportsynthesissynthesis=synthesis()audio=synthesis.synthesis("Привет, Мир! Я умею озвучивать цифры и символы! Например: 2 + 2")sd.play(audio,synthesis.sample_rate)time.sleep(len(audio)/synthesis.sample_rate)sd.stop()Пример 2 функции:fromsilero_advancedimportsynthesissynthesis=synthesis()synthesis.synthesisAndPlay("Привет, Мир! Я умею озвучивать цифры и символы! Например: 2 + 2")Пример 3 функции:fromsilero_advancedimportsynthesissynthesis=synthesis()synthesis.synthesisAndSaveWav("Привет, Мир! Я умею озвучивать цифры и символы! Например: 2 + 2")# Файл будет сохранен в корневом каталоге🙇 Спасибо всем авторам библиотек, что были использованы в данном проекте:SileroSergey Prokhorov
Package: silero-api-server
A simple FastAPI Server to run Silero TTSCredit goes to the developers of Silero TTSSilero PyTorch PageSilero GitHub PageThis is primarily to serve the TTS extension inSillyTavern. The TTS module or server can be used any way you wish.Installationpip install silero-api-serverStarting Serverpython -m silero_api_serverwill run on default ip and port (0.0.0.0:8001)usage: silero_api_server [-h] [-o HOST] [-p PORT]

Run Silero within a FastAPI application

options:
  -h, --help            show this help message and exit
  -o HOST, --host HOST
  -p PORT, --port PORTOn first run of server, two operations occur automatically. These may take a minute or two.The model will be downloadedVoice samples will be generated.API DocsAPI Docs can be accessed fromhttp://localhost:8001/docsVoice SamplesSamples are served statically by the web server at/samples/{speaker}.wavor callable from the API from/tts/sample?speaker={speaker}endpoint.Selecting LanguageDownload and set the desired language usingPOST /tts/languagewith payload{"id":"languageId"}List of language ids are available viaGET /tts/language
Package: silero-vad-fork
Silero VADSilero VAD- pre-trained enterprise-gradeVoice Activity Detector(also see ourSTT models).Real Time Examplehttps://user-images.githubusercontent.com/36505480/144874384-95f80f6d-a4f1-42cc-9be7-004c891dd481.mp4Key FeaturesStellar accuracySilero VAD hasexcellent resultson speech detection tasks.FastOne audio chunk (30+ ms)takesless than1msto be processed on a single CPU thread. Using batching or GPU can also improve performance considerably. Under certain conditions ONNX may even run up to 4-5x faster.LightweightJIT model is around one megabyte in size.GeneralSilero VAD was trained on huge corpora that include over100languages and it performs well on audios from different domains with various background noise and quality levels.Flexible sampling rateSilero VADsupports8000 Hzand16000 Hzsampling rates.Flexible chunk sizeModel was trained on30 ms. Longer chunks are supported directly, others may work as well.Highly PortableSilero VAD reaps benefits from the rich ecosystems built aroundPyTorchandONNXrunning everywhere where these runtimes are available.No Strings AttachedPublished under permissive license (MIT) Silero VAD has zero strings attached - no telemetry, no keys, no registration, no built-in expiration, no keys or vendor lock.Typical Use CasesVoice activity detection for IOT / edge / mobile use casesData cleaning and preparation, voice detection in generalTelephony and call-center automation, voice botsVoice interfacesLinksExamples and DependenciesQuality MetricsPerformance MetricsVersions and Available ModelsFurther readingFAQGet In TouchTry our models, create anissue, start adiscussion, join our telegramchat,emailus, read ournews.Please see ourwikiandtiersfor relevant information andemailus directly.Citations@misc{Silero VAD,
  author = {Silero Team},
  title = {Silero VAD: pre-trained enterprise-grade Voice Activity Detector (VAD), Number Detector and Language Classifier},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/snakers4/silero-vad}},
  commit = {insert_some_commit_here},
  email = {hello@silero.ai}
}Examples and VAD-based Community AppsExample of VAD ONNX Runtime model usage inC++Voice activity detection for thebrowserusing ONNX Runtime Web
Package: silex
A bunch of useful functions for string manipulation.Install (automatic)$ pip install silexInstall (manual)$ git clone
$ cd silex
$ python setup.py installUsageIn[1]:fromsileximport*In[2]:printchop("whitespace",5)['white','space','']In[3]:printnumberex('1.2euros')1.2In[4]:printsurround('b','<','>')<b>In[5]:printstrip_html('<p><i>hello</i></p>')helloIn[6]:printunslugify('sex_appeal')sexappealIn[7]:printslugify(u'je suis\ épuisé')je-suis-epuiseIn[8]:printclean('hello\nword\n')hellowordIn[9]:printkebabcase('hello  world')hello-worldIn[10]:printpascalcase('hello world')HelloWorldIn[11]:printcamelcase('hello world')helloWorldIn[12]:printsnakecase('hello world')hello_worldLicenseMIT
Package: silhouette2wireframe
silhouette2wireframeConvert silhouette image to wireframe distorted by Perlin noiseInstallationpip install silhouette2wireframeConvert silhouette image to a wireframefrom silhouette2wireframe import ImageToWireframe

converter = ImageToWireframe("silhouette.png", size=(1080,1350))

colors = {"bg": "#000022", "top": "dodgerblue", "bottom": "white"} 
fig = converter.draw_frame(0, cdict=colors)
plt.show()
Package: silica-django
# silica-django
A Django app which translates Django forms data into JSONSchema and UISchema as expected by the jsonforms frontend library.## Installation
1. Install the library (pip install silica-django)
2. Add“silica_django”to yourINSTALLED_APPS## Sample Project
A sample project demonstrating simple usage of this library, using the companion frontend library [Silica for Vue](https://github.com/zagaran/silica-vue), can be found [here](https://github.com/zagaran/sample-silica-django-app).## Tests
There is a (relatively sparse) test suite which comes with this library to prevent regression. To run it, simply runpython silica_django/tests.pyfrom the root directory.## Documentation
There is a combined documentation repository for all Silica libraries located at [Silica-Docs](https://www.github.com/zagaran/silica-docs).
Package: silicon
hyperdrive: Robinhood analytics and algorithmic tradinghyperdriveis a project to obtain stock data, create trading strategies, test against historical data (backtesting), and deploy strategies for algorithmic trading.Getting StartedPrerequisitesYou will need Python 3.8+ and a Robinhood account.Place your credentials in a file named.envin the project root directory.
Follow this structure:RH_USERNAME=...
RH_PASSWORD=...
RH_2FA=...
IEXCLOUD=...InstallationTo install the necessary packages, runpip install -r requirements.txtUseMaking ScriptsTo make a script, create a new .py file in thescripts/dir with the following code:import sys
sys.path.append('src')
from Algotrader import HyperDrive  # noqa autopep8

drive = HyperDrive()Features:Broker authenticationAutomated data storageBacktesting engineMonte Carlo simulationsPlotting and technical analysisModel trainingStrategy definition (start with buy and hold)Buy and sell functionalityLive tradingDocumentationCheck out theRoadmapfor progress
...AuthUsing Robinhood 2FA, we can simply provide our MFA one-time password in the.envfile to login to Robinhood (viapyotp).DataPrice and VolumeActionsMergersBuybacksSentimentNews SentimentInstitutional SentimentAnalyst RecommendationsCompany / MicroProfile (Sector, # of Employees)EarningsCash FlowCEO CompensationGovernment / MacroUnemployment RateReal GDPUS Recession ProbabilitiesMarketGeneral Volatility (VIX)Sector Performance
Package: silicon-clustering
A fast algorithm computing connectivity components (clusters) of elements based
on their feature cosine similarity. Two elements are connected by an edge if
their cosine similarity is above a given threshold. Accepts either a numpy
2D array or a scipy column-sparse (CSR) matrix.Install fromPyPIwithpip installsilicon-clusteringTomáš Gavenčiakgavento@ucw.czUsage exampleimport silicon, numpy
# 1000 rows, 10 features
data = numpy.random.rand(1000, 10)
# The ensemble normalizes rows of the data by default
# Choose high verbosity (via logging), cosine similarity >=0.97
ens = silicon.CosineClustering(data, sim_threshold=0.97, verbosity=2)
ens.run()
# (... progress reports)
ens.clusters[0]
# <Cluster no 0, size 13>
ens.clusters_by_size()[0]
# <Cluster no 36, size 22>
# With pyplot you can see the projected points
import matplotlib.pyplot as plt
ens.plot(); plt.show()
# or the individual clusters
ens.clusters_by_size()[0].plot(ens); plt.show()DetailsThe algorithm uses several tricks to speed up the computation compared to traditional
all-pair scalar products via matrix multiplication:The data is projected intocell_dimsprincipal components (PCA) by feature. The
projection divides the data into cells of sizeself.distanceso only rows from
adjacent cells have a chance to be similar enough. Then the vectors of the rows of
the adjacent cells are multiplied.This would still mean that some cells are too big for matrix multiplication so a second
trick is used before the cell multiplication: nibbling at cca 1000 random points of the
dataset. For a random center row, the similarities to all other rows are computed and
all similar points are clustered together (possibly merging existing clusters). This has
the effect of pre-clustering most dense points of the dataset (esp. repeated values)
- dense clusters have a good chance to be hit with a center, eliminating most of the
mass of the cluster (as well as the respective cells). The number of nibble
iterations should be tuned according to data (to reasonably decrease the cell size).To combine the two tricks, a portion of the clustered points (e.g. 10%) together with
all the unclustered points are considered for adjacent cell multiplication. The 10%
returned points should ensure that the nibbled clusters are linked with any points not
hit by the nibble but close to and in effect belonging to the clusters.Since not all nibbled rows are used in the adjacent cell scalar product, the algorithm
may miss few individual cluster connections at nibble ball boundaries, but we found it
unlikely in practice.The algorithm clusters 10000 gaussian points in 30 dimensions in under a second.
Package: silicone
Silicone is a Python package which can be used to infer emissions from other emissions data.
It is intended to ‘infill’ integrated assessment model (IAM) data so that their scenarios
quantify more climate-relevant emissions than are natively reported by the IAMs themselves.
It does this by comparing the incomplete emissions set to complete data from other sources.
It uses the relationships within the complete data to make informed infilling estimates of
otherwise missing emissions timeseries.
For example, it can add emissions of aerosol precurors based on carbon dioxide emissions
and infill nitrous oxide emissions based on methane, or split HFC emissions pathways into
emissions of different specific HFC gases.LicenseSilicone is free software under a BSD 3-Clause License, seeLICENSE.FundersThis project has received funding from the European Union Horizon 2020 research and
innovation programme under grant agreement No 820829 (CONSTRAIN) and No 641816 (CRESCENDO).
Package: siliqua
Siliqua[![image](https://img.shields.io/pypi/v/siliqua.svg)](https://pypi.org/project/siliqua/)
[![Coverage Status](https://coveralls.io/repos/github/Matoking/siliqua/badge.svg?branch=master)](https://coveralls.io/github/Matoking/siliqua?branch=master)
[![Build Status](https://travis-ci.com/Matoking/siliqua.png?branch=master)](https://travis-ci.com/Matoking/siliqua)
[![image](https://readthedocs.org/projects/siliqua/badge/?version=latest)](https://siliqua.readthedocs.io/en/latest/?badge=latest)Modular light wallet for the NANO cryptocurrency with a command-line interface.This application is under early development and any features are subject to change at the moment. Use at your own risk.FeaturesOptional two-level encryption
* Wallet secrets (private keys and seed) and the wallet itself can be encrypted separatelyWatching-only accountsSupports NANO seedsTransaction timestamps and descriptionsPortable wallet files for easy backups with timestamps and details intactNANO node and NanoVault server supportJSON output for easy integration with scriptsInstallationYou can install the reference command-line interface using pip:` pip install siliqua `Siliqua requires a working build environment for the C extensions. For example, on Debian-based distros you can install the required Python header files and a C compiler using the following command:` apt installbuild-essentialpython3-dev`virtualenv is recommended instead of a system-wide installation. For example, to create a virtualenv and install Siliqua inside it:` python3-mvenvvenv source venv/bin/activate pip install siliqua `To deactivate the virtualenv, run:` deactivate `After installation, you can read the [Getting Started](https://siliqua.readthedocs.io/en/latest/user/getting_started.html) section of the documentation.DocumentationFor user and developer documentation, see online documentation at [Read the Docs](https://siliqua.readthedocs.io/en/latest/).You can also build the documentation yourself by runningpython setup.py build_sphinx.Donationsxrb_35xiwe88pqxemiwffi5hrgwyujxg1su948zh8zhczdwwc75bqjb13fhddhke
Package: silizium-binerdy
Travis CICoverallsTravis build historyswt-19fs-siliziumSource code for the wiki philosopy game in the module swt fs19geckodriverYou need to download and add geckodriver to your path environment. You can get it here:https://github.com/mozilla/geckodriver/releasesinstallCode is released underhttps://pypi.org/project/silizium-binerdy/pip install silizium-binerdycli usageWindows users: Use MINGW64 (git bash) console and copy your python.exe and rename the copy to python3.exe in your python installation folder. Then you should be able to execute following command.silizium-cli.py -w "Hund" -l 20
Package: silkaj
SilkajPowerfull, lightweight, and multi-platform command line client written with Python for Ğ1 and Ğ1-Test currenciesWebsiteInstallDistributionInstall with your favorite package manager. See below thepackaging status paragraph.PipxIf you want a more recent versioninstall with pipx:sudoaptinstallpipx
pipxinstallsilkajDocker imagesThere is two kind of images. One build withpipfor user purposes, and one using Poetry for developer purposes.Docker imagesFor contributing purposesInstall the Poetry development environmentCheck out thecontributing guidelinesUsageGet help usage with-hor--helpoptions, then run:silkaj<sub-command>Will automatically request and post data onduniter.org 443main Ğ1 node.Specify a custom node with-epoption:silkaj-ep<hostname>:<port><sub-command>FeaturesCurrency information & blockchain explorationCheck the present currency information standDisplay current proof of work difficulty level to generate the next blockExplore the blockchain block by blockVerify blockchain blocks hashesMoney managementTransaction emissionMulti-recipients transaction supportRead transaction recipients and amounts from a fileConsult wallets balancesConsult wallet historyWeb-of-Trust managementLook up for public keys and identitiesCheck sent and received certifications and consult the membership status of any given identity in the Web of TrustCertification emissionMembership emissionRevocation file handlingAuthenticationAuthentication methods: Scrypt, file, and (E)WIFOthersDisplay Ğ1 monetary licensePublic key checksumWrappersHow-to: automate transactions and multi-outputTransaction generator written in ShellĞ1CotisG1pourboireĞ1SMSĞmixerprintqrjuneDependenciesSilkaj is based on following Python modules:Click: Composable command line interface toolkitDuniterPy: Most complete client oriented Python library for Duniter/Ğ1 ecosystemPendulum: Datetimes made easytexttable: Creation of simple ASCII tablesNamesI wanted to call that program:bamiyanmargouillatlsocietycashmereI finally called itSilkajasSilkin esperanto.WebsiteSilkaj website sourcesPackaging status