Saravanan666/pypi-org huggingface dataset:
Package: siege-engine
siege-engineA general basic Python-powered SYN flooder bot.Installation and operationJust type:pipinstallsiege-engine
python-msiege_engine300tass.comNote that a helpful error message will be displayed if you just type:python-msiege_enginePlease set the 300 value regarding your network conditions. This is the amount of threads
that will be spawned. Obviously, more is better (I bet your network could hold about 2000
of them).Note that increasing thread count about 100% CPU usage is pointless, because of the GIL.
You can, altogether, fire up multiple instances :DJust note that GIL is not a practical limit, I've tested it on my PC with 15k threads and it ate
only 23% of the CPU, so it's more academic concern.Note that if zero connections were made, that means the target server is no longer responding,
which is a good thing.Change logv2.0: Better messagesv1.9: added a total counter for wasted bytesv1.8: fixed problems with terminationv1.7: Removed scapyv1.6: Added scapyv1.5: SSL socket will be picked upon nowv1.4: fixed a critical bugv1.3: significantly refactored the codev1.2: since v1.1 we're been hosted on PyPI, removed these nasty entry pointsv1.1: added graceful support for time-out sessionsHow to adjust thread count.Start from 13000. The startup time for 25000 threads is pretty rough, but overall the app manages to start.
If the process manages to hit a single core 100%, start a new program, or just reduce
the amount of threads. The process is network-bound.Valid targetstass.comkremlin.rueng.putin.kremlin.rueng.constitution.kremlin.rueng.flag.kremlin.rulenta.rugovernment.rurt.comach.gov.rucustoms.rueconomy.gov.rufas.gov.rufedsfm.rugks.ruminfin.runalog.rurosreestr.rurosreserv.rugosbar.gosuslugi.rucikrf.rucouncil.gov.ruduma.gov.rugovernment.rukremlin.ruopen.gov.rugusp.gov.ruudprf.rufapmc.rurkn.gov.rufso.gov.rugfs.rurossvyaz.rudigital.gov.rufstec.rurs.gov.rufsvts.gov.rumid.rurussiatourism.ruombudsmanrf.orgrosmintrud.rurostrud.ruarchives.rufsa.gov.rugost.rufadm.gov.ruobrnadzor.gov.rufederalspace.rumkrf.rurupto.ruedu.gov.ruminobrnauki.gov.ruminvostokrazvitia.rumil.runac.gov.rusvr.gov.rufsb.rumchs.gov.ruscrf.gov.rurosavtodor.rufavt.rumintrans.rumorflot.rurostransnadzor.ruroszeldor.rufsin.sugenproc.gov.ruksrf.rumvd.rurosgvard.rufssprus.ruminjust.ruxn--b1ab2a0a.xn--b1aew.xn--p1airpn.gov.ruvoda.mnr.gov.rufishcom.rufsvps.rugosnadzor.rumcx.rumeteorf.rumnr.gov.rurosleshoz.gov.rurosnedra.gov.rufmbaros.rurosminzdrav.rurospotrebnadzor.ruminsport.gov.ruroszdravnadzor.ruminenergo.gov.ruminpromtorg.gov.ruminstroyrf.rudata.gov.rupartners.gosuslugi.rugosuslugi.ruIf you have any better ideas, PRs are welcome!Usage via DockerYou can use the excellent Docker image athttps://github.com/maksym-iv/battle-tools#siege-engine.
Package: siegfried
siegfried ====================== Tools for taming lynx.Installpip install siegfriedTestrequiresnosenosetestsUsageThis module contains various methods that are used throughoutnewslnyx. but the main functions areunshorten_url,is_article_url, andprepare_url:fromsiegfriedimport(unshorten_url,is_article_url,prepare_url)printunshorten_url('bit.ly/1j3SrUC')# http://towcenter.org/blog/tow-fellows-brian-abelson-and-michael-keller-to-study-the-impact-of-journalism/printis_article_url('http://towcenter.org/blog/tow-fellows-brian-abelson-and-michael-keller-to-study-the-impact-of-journalism')# Trueprintis_article_url('http://towcenter.org/blog/tow-fellows-brian-abelson-and-michael-keller-to-study-the-impact-of-journalism',pattern=r'.*towcenter\.org/blog/.*')# Trueimportrepattern=re.compile(r'.*towcenter\.org/blog/.*')printis_article_url('http://towcenter.org/blog/tow-fellows-brian-abelson-and-michael-keller-to-study-the-impact-of-journalism',pattern=pattern)# Trueprintprepare_url('http://towcenter.org/blog/tow-fellows-brian-abelson-and-michael-keller-to-study-the-impact-of-journalism/?q=lfjad&f=lkfdjsal')# http://towcenter.org/blog/tow-fellows-brian-abelson-and-michael-keller-to-study-the-impact-of-journalism
Package: sielc-dompser
Sielc DompserThis is an  unofficial Python package to interface with SIELC Technologies, Inc products through DOMP (Device Objected Manager Protocol) serial communication. We are not affiliated with SIELC.Products supported:Miniature autosamplerBased on the Autosampler serial protocol Rev. 110. Seethis documentfor detailsMiniature autosamplerSeeherefor an example of how to interface and control a miniature autosamplerNote that not all the functionality as specified in the protocol document has been implemented, and on testing, some
protocols did not work.
Package: siemkit
CyberSIEM(R)Community KitAn open-source "Swiss Army Knife" for anything SIEM related!While this project is gaining rapid progress, it is far from ready..WARNING!This toolkit is still experimental and we do not recommended it for use in production. Do note that
at this stage we are not yet releasing stable versions as the API changes on daily basis!InstallationpipinstallsiemkitUpdatingpipinstall--upgradesiemkitPackage:https://pypi.org/project/siemkit/GitHub:https://github.com/cybersiem/communityLibrariesWork in progresssiemkit.api.arcsight.esm.v72siemkit.arcsightsiemkit.simulatesiemkit.win32.registrysiemkit.adaptorssiemkit.cfgsiemkit.constsiemkit.datasiemkit.eventsiemkit.filesiemkit.flagsiemkit.generatesiemkit.hashsiemkit.htmlsiemkit.ldapsiemkit.listensiemkit.loggingsiemkit.netsiemkit.parsesiemkit.randomsiemkit.sendsiemkit.smtpsiemkit.timesiemkit.webWhy ContributeWhy Contribute to Open-SourceWork in progress . . .Contributors / Authors@DK26-David Krasnitsky(CyberSIEM)Ofek Sher(CyberSIEM)Yochay Ezra(CyberSIEM)Special ThanksMarius2(Micro Focus Expert)Third-Party Libraries:hfilesize - MIT Licenseby: simonzacksource:https://github.com/simonzack/hfilesizelicense:https://opensource.org/licenses/MITpytimeparse - MIT Licenseby: wrobertssource:https://github.com/wroberts/pytimeparselicense:https://github.com/wroberts/pytimeparse/blob/master/LICENSE.rstdateparser - BSD 3-Clause Licensesource:https://github.com/scrapinghub/dateparserlicense:https://github.com/scrapinghub/dateparser/blob/master/LICENSErequests - Apache 2.0 Licenseby: Kenneth Reitzsource:https://github.com/psf/requestslicense:https://github.com/psf/requests/blob/master/LICENSEfuture - MIT Licenseby: Ed Schofieldhomepage:https://python-future.org/license:https://opensource.org/licenses/MITsetuptools - MIT Licenseby: Jason R Coombsjaraco@jaraco.comsource:https://github.com/pypa/setuptoolslicense:https://opensource.org/licenses/MITurllib3 - MIT Licenseby: Andrey Petrovandrey.petrov@shazow.nethomepage:https://urllib3.readthedocs.io/en/latest/source:https://github.com/urllib3/urllib3license:https://github.com/urllib3/urllib3/blob/master/LICENSE.txt
Package: siena
Efficient entity annotation tool for Sinhala, English, or Sinhala-English code-switched text corpora.FeaturesAllows annotating both Sinhala and English textual dataFully compatible with Rasa 2.8.x NLU training data filesAllows exporting annotated NLU YAML filesAble to auto annotate entities efficiently based on novel NLP techniques including reverse-stemmingEasy to use SIENA CLI that can spin up a GUI server, locallyRead more ondocsOngoing ResearchConcurrent entity tagging for multiple usersImport/Export support for non-Rasa NLU data files and text corporaKnown IssuesSupport for Rasa versions other than 2.8.x is under ongoing developmentBenchmark tests are in progressðŸ“’ Docs:https://siena-nlp.github.ioðŸ“¦ PyPi:https://pypi.org/project/siena/1.0.2/ðŸªµ Full Changelog: Refer the relevant GitHub branch (v1.0.0)
Package: siena3d
Siena3DScopeSiena3D is a tool to analyse the morphology of the ionised gas emission in 3D spectroscopic data cubes.
It uses a set of emission lines to decompose an input spectrum and trace the components via their kinematics.
This allows to resolve and  characterize the individual components with a sub-pixel resolution,
well below the width of the point spread function.Siena3D was originally developed to analyse the extended emission line region (EELR) in unobscured AGN obtained with
ESO VLT/MUSE. However, the method may also be applied to other science cases where emission line spectra are traced in
3D cubes obtained with other IFS instruments.InstallationThe easiest way to install is to first create a dedicated conda environment prior to the installation:(1) conda create --name siena3d python=3.9
(2) conda activate Siena3DIn order to install Siena3D, one can obtain the package from PyPi withpip install siena3dIn addition, the user needs to manually install the Maoppy package with the commnandpip install git+https://gitlab.lam.fr/lam-grd-public/maoppy.git@masterMore information on the Maoppy package can be found:here.Basic usageThe Siena3D code can be run entirely from the command line.  It is set up such that the files are placed in the "Input"
and "Output" subdirectories of the working directory.
A demo is provided in the example directory where also the parameter files together with a set of example data cube are provided.
To test the installation and run the example execute:(1) Go to the example directory
(2) python runall.pyWe refer to the Siena3D user manual for more information on running Siena3D and the underlying algorithms.Manual for more information on parameter file setupMore details on the underlying algorithms and principles of Siena3D can be found in theSiena3D User Manual.
It provides an overview of the options and tips
on how to setup the parameter files and specifies the required input data format
structure to run Siena3D from the command line.
Package: siena_mls
Siena-MLS : Multimedia Library for StudentsThe project is a development platform designed forMedia Computation. It offers a pure Python implementation ofgatech-csl/jes, aiming to ensure consistent API functionality across both implementations. Notable enhancements beyond the foundational versions are detailed in subsequent sections. This package is compatible with any Python3.10^ version, allowing students to utilize the Python programming language to manipulate multimedia components, including images, sounds, and videos over online repl providers. The current API documentation aligns with the JES usage described in the reference book, and the functions implemented to date are provided here.This library was developed for the Introduction to Computer Science (CSIS 110) course at Siena College, as well as the college's affiliated high school computer science programs.UsageTo install the package in your projects, use pip / poetry / upm etc. The best way is to add the following to your pyproject.toml file:[tool.poetry.dependencies]python=">=3.10.0,<3.11"siena_mls=">18"# This is the MLS Version ( check with the latest one )If you just want to use this on a script, this will install it in your python environment.pipinstallsiena_mlsFinalyin the first line of your main file:fromsiena_mlsimport*Contributin and DeployingThe project is configured to auto deploy to PyPi on project release. Here is a possible sequence of actions that may help.ContributionsUse github codespaces / local VSCode to make changes.Contribute any changes, test things out inplayground.py.Add files and Commit then push.Version Update:: Increment Version inpyproject.toml.Pre-Release Deployment:Create a pre-release in GitHub to push totest.pypi.org. Just confirm that the Actions ran and the version is avaliable over test.pypiRelease Deployment:Create a release in GitHub to push topypi.org.Refer to comamnds in Annex if you need to manually build / test / publish.Support & GrantsContributions-Robin Flatland & Ninad Chaudhari (Siena College)AnnextureAlternate: not using pyTestThe same can be acheved without using pytestpoetry run coverage run -m unittest discoverpoetry run coverage htmlInstalling dependenciespip install .can parsepyproject.tomland install all deps in current python environment.Project Commands & ScriptsThis project utilizesPoetryfor efficient package & dependencies management. Below is a quick guide to the most commonly used commands:Install Dependencies:poetry installBuild Project:poetry buildTest:poetry run pytest
Package: siepic-ebeam-pdk
SiEPIC_EBeam_PDKDeveloped as part of theSiEPICprogram, the SiEPIC EBeam PDK is a process design kit (PDK) for fabrication using electron beam lithography, and a library silicon photonics components.Instruction on design, layout, fabrication, test, data analysis for silicon photonics provided in the edX course:Silicon Photonics Design, Fabrication and Data Analysisand textbookSilicon Photonics Design: From Devices to Systemsby Lukas Chrostowski and Michael Hochberg.Download and Installation instructions:Details are provided in the Wiki page:Installation instructions. KLayout's package manager is used for both downloading and installing the package into the KLayout Application.The PDK may also be installed using pip install siepic_ebeam_pdk
Package: sierraecg
sierra-ecg-toolsSierra ECG Tools for Python.RequirementsPython 3.8+.DependenciesDependencies are defined in:pyproject.tomlExamplefromsierraecgimportread_filef=read_file('path/to/file.xml')forleadinf.leads:print(f"{lead.label}: dur={lead.duration}f={lead.sampling_freq}{lead.samples[0:8]}...")
Package: sierras
sierrassierrasis a tool for empirical Arrhenius equation fitting for
thermally-induced physicochemical processes.RequirementsYou need Python 3.8+ to run sierras.InstallationYou can install the most recent stable release of sierras withpippython -m pip install -U pip
python -m pip install -U sierrasUsageA simple example of use:fromsierrasimportArrheniusRegressor# default constant is Boltzmann in eV/Kareg=ArrheniusRegressor()# temperatures and target_process arrays-like as usually used in scikit-learnareg.fit(Temperatures,target_process)# print the activation energy ([eV] in the default case) and the extrapolated# process at room temperatures values (in the same units as target_process is)print(areg.activation_energy_,areg.extrapolated_process_)# plot the fittingfig,ax=plt.subplots()areg.plot(ax=ax)For a more detailed explanation you can read thetutorialand theAPI.LicenseMIT LicenseContact infoYou can contact me atffernandev@gmail.com
Package: sietch
sietchSietch is a lightweight environment CLI tool.
It creates and manages local environments for simple projects.
It is designed to be used for small hobby projects that may share some basic environment requirements.InstallationpipinstallsietchUsageCreate a new environmentsietchcreate--nameMyEnvActivate an environmentActivation is not fully implemented, the command here prints the activation command.sietchactivate--nameMyEnvRemove an environmentsietchremove--nameMyEnvList all environmentssietchlist
Package: sieve
Ripped fromFormEncodeandstrainerjust to support Pythons 2 and 3.
Intended for use in your webapp test suites.Build StatusBranchStatusmasterdevelopFor ExampleThere are two main functions you might care to use:eq_xmlandin_xml:>>> from sieve.operators import eq_xml, in_xml
>>> a = "<foo><bar>Value</bar></foo>"
>>> b = """
... <foo>
...     <bar>
...         Value
...     </bar>
... </foo>
... """
>>> eq_xml(a, b)
True
>>> c = "<html><body><foo><bar>Value</bar></foo></body></html"
>>> in_xml(a, c)  # 'needle' in a 'haystack'
TrueThere are also two sibling convenience functions:assert_eq_xmlandassert_in_xml.
Package: sieve-git-pushdeploy
# Sieve git push deployment
[![Build Status](https://travis-ci.org/mheistermann/sieve-git-pushdeploy.svg?branch=master)](https://travis-ci.org/mheistermann/sieve-git-pushdeploy)## IntroductionKeep your sieve scripts in a git repos (e.g. on your mailserver), push
to them to deploy a new version.Warning: experimental, use at your own risk. Probably quite buggy, just hacked
this together quickly.## Installation### Installation with pip`pip3 installsieve-git-pushdeployHOOK=/usr/local/bin/sieve_git_pushdeploy`### Installation without pip` virtualenv-ppython2 .venv source .venv/bin/activate pip install-rrequirements.txtHOOK=$(pwd)/.venv/bin/sieve_git_pushdeploy`Copy the provided example copy to~/.config/sieve-git-pushdeploy/sieve.confand adjust the values.### Setup a sieve repositorySomewhere,` git init--baresieve.git cd sieve.git/hooks ln-s$HOOK update `You can also opt to only use a subset of the hooks.Now if you clone that repo and push your configs to it, they will be checked
and uploaded automatically.## Licensesieve-git-pushdeploy was written by Martin Heistermann <github()mheistermann.de>
and is available under the terms of the GPL v3.## IdeasColored output that stands out from the rest of the git output## ContributingSend me a pull request on github or [email me a patch](mailto:github[]mheistermann.de).
Package: sievelib
Client-side Sieve and Managesieve library written in Python.Sieve : An Email Filtering Language
(RFC 5228)ManageSieve : A Protocol for Remotely Managing Sieve Scripts
(RFC 5804)InstallationTo installsievelibfrom PyPI:pip install sievelibTo install sievelib from git:git clone git@github.com:tonioo/sievelib.git
cd sievelib
python ./setup.py installSieve toolsWhat is supportedCurrently, the provided parser supports most of the functionalities
described in the RFC. The only exception concerns section2.4.2.4. Encoding Characters Using “encoded-character”which is not
supported.The following extensions are also supported:Copying Without Side Effects (RFC 3894)Body (RFC 5173)Vacation (RFC 5230)Relational (RFC 5231)Imap4flags (RFC 5232)The following extensions are partially supported:Date and Index (RFC 5260)Checking Mailbox Status and Accessing Mailbox Metadata (RFC 5490)Extending the parserIt is possible to extend the parser by adding new supported
commands. For example:import sievelib

class MyCommand(sievelib.commands.ActionCommand):
    args_definition = [
        {"name": "testtag",
            "type": ["tag"],
            "write_tag": True,
            "values": [":testtag"],
            "extra_arg": {"type": "number",
                          "required": False},
            "required": False},
        {"name": "recipients",
            "type": ["string", "stringlist"],
            "required": True}
    ]

sievelib.commands.add_commands(MyCommand)Basic usageThe parser can either be used from the command-line:$ cd sievelib
$ python parser.py test.sieve
Syntax OK
$Or can be used from a python environment (or script/module):>>> from sievelib.parser import Parser
>>> p = Parser()
>>> p.parse('require ["fileinto"];')
True
>>> p.dump()
require (type: control)
    ["fileinto"]
>>>
>>> p.parse('require ["fileinto"]')
False
>>> p.error
'line 1: parsing error: end of script reached while semicolon expected'
>>>Simple filters creationSome high-level classes are provided with thefactorymodule, they
make the generation of Sieve rules easier:>>> from sievelib.factory import FiltersSet
>>> fs = FiltersSet("test")
>>> fs.addfilter("rule1",
...              [("Sender", ":is", "toto@toto.com"),],
...              [("fileinto", "Toto"),])
>>> fs.tosieve()
require ["fileinto"];

# Filter: rule1
if anyof (header :is "Sender" "toto@toto.com") {
    fileinto "Toto";
}
>>>Additional documentation is available within source code.ManageSieve toolsWhat is supportedAll mandatory commands are supported. TheRENAMEextension is
supported, with a simulated behaviour for server that do not support
it.For theAUTHENTICATEcommand, supported mechanisms areDIGEST-MD5,PLAINandLOGIN.Basic usageThe ManageSieve client is intended to be used from another python
application (there isn’t any shell provided):>>> from sievelib.managesieve import Client
>>> c = Client("server.example.com")
>>> c.connect("user", "password", starttls=False, authmech="DIGEST-MD5")
True
>>> c.listscripts()
("active_script", ["script1", "script2"])
>>> c.setactive("script1")
True
>>> c.havespace("script3", 45)
True
>>>Additional documentation is available with source code.
Package: sifcoin-hash
sifcoin-hash - bindings for Proof of Work hash function used by SifcoinSifcoin ANN on [bitcointalk ru thread](https://bitcointalk.org/index.php?topic=240884.0) …Hash signature function block header changed from SHA-256(SHA-256()) to a daisy-chain of the candidates / finalists and winner of NIST, BLAKE, BMW, Grøstl, JH, Keccak, Skein. All feature 512-bit but the end result is truncated to 256 bits. The rationale for avoiding SHA-256 and scrypt is to protect against possible startup sharp influx and then just as sharp outflow capacity miners with other currencies and the subsequent “paralysis”. Complication chain to the length of 6 different hash functions and increase the intermediate bit depth to 512 as an attempt to keep from further development of highly efficient Mh/s GPU algorithms and threat of “simple” Gh/s ASIC devices.Python bindings for Sifcoin hashing.Typical usage:def block_header_hash(chain, header):
    return sifcoin_hash.getPoWHash(header)Module installationInstall module with:$ pip install sifcoin-hashor:$ pip install git+https://github.com/gjhiggins/sifcoin-hash --allow-externalor:$ git clone https://github.com/gjhiggins/sifcoin-hash
$ cd sifcoin-hash
$ python setup.py install
Package: sifeng
Si Feng: The machine learning quant research frameworkDocumentation:Getting Started|API ReferenceReleases:PyPI|Source|ChangelogSifeng (思风, imported assifeng) is a quant research framework built by our team, which also share the same name Sifeng. It is a python package mainly designed to be used within our team but also welcomes public users. Feel free to tell us what features you want to add to this project. Currently it supports many features such as::book: Detailedwiki, and annotations.:chart_with_upwards_trend: Accessible and easy to use function for retrieving structurized data.:zap: SOTA deep learning models, bayesian nerual networks, useful tools, and so on.Table of ContentsTable of ContentsInstallationHighlightsInstallationIt is recommended that you install usingpip, and make sure to install the latest possible version to aviod any problems:$pipinstallsifeng# normal install$pipinstall--upgradesifeng# upgrade if neededAlternatively, it is also possible to install sifeng by cloning this repository:$gitclonehttps://github.com/zty200489/sifeng.git
$cdsifeng
$pipinstall.sifeng requires the following dependencies:NumpyPandasDuckdbjoblibrequeststqdmpytorchcoloramaHighlights:chart_with_upwards_trend: We understand that in quant resarch, data is the key. So we provide easy-to-use API for retrieving data from remote OSS server. We currently only supports daily HLOC data, indicators and basic information, but we will include other kinds of data shortly in the future.:handshake: As out deep-learning models are based on torch, we support uniform and consistent APIs that can be pieced easily into your eexisting projects to ease you the burden of repetitive hard work.:bookmark_tabs: Detailed and fully-customizeable logs that fits specifically for your project. You can easily design your own log format or print custom log messages by inheriting theVerboseBaseclass and passing it into an trainer.:abacus: Model is the second most important part when doing quant researches. So we provide SOTA deep-learning models includingMultiheadedSelfAttentionModule,MixtureOfExpertsBlockand even Bayesian modules to help you conduct research across various kinds of data.