Saravanan666/pypi-org huggingface dataset:
Package: scapy
ScapyScapy is a powerful Python-based interactive packet manipulation program and
library.It is able to forge or decode packets of a wide number of protocols, send them
on the wire, capture them, store or read them using pcap files, match requests
and replies, and much more. It is designed to allow fast packet prototyping by
using default values that work.It can easily handle most classical tasks like scanning, tracerouting, probing,
unit tests, attacks or network discovery (it can replacehping, 85% ofnmap,arpspoof,arp-sk,arping,tcpdump,wireshark,p0f, etc.). It also
performs very well at a lot of other specific tasks that most other tools can't
handle, like sending invalid frames, injecting your own 802.11 frames, combining
techniques (VLAN hopping+ARP cache poisoning, VoIP decoding on WEP protected
channel, ...), etc.Scapy supports Python 2.7 and Python 3 (3.4 to 3.9). It's intended to
be cross platform, and runs on many different platforms (Linux, OSX,
*BSD, and Windows).Getting startedScapy is usable either as ashellor as alibrary.
For further details, please head over toGetting started with Scapy, which is part of the documentation.Shell demoScapy can easily be used as an interactive shell to interact with the network.
The following example shows how to send an ICMP Echo Request message togithub.com, then display the reply source IP address:sudo./run_scapyWelcometoScapy>>>p=IP(dst="github.com")/ICMP()>>>r=sr1(p)Beginemission:.Finishedtosend1packets.*Received2packets,got1answers,remaining0packets>>>r[IP].src'192.30.253.113'ResourcesThedocumentationcontains more
advanced use cases, and examples.Other useful resources:Scapy in 20 minutesInteractive tutorial(part of the documentation)The quick demo: an interactive session(some examples may be outdated)HTTP/2 notebookTLS notebooksInstallationScapy works without any external Python modules on Linux and BSD like operating
systems. On Windows, you need to install some mandatory dependencies as
described inthe
documentation.On most systems, using Scapy is as simple as running the following commands:gitclonehttps://github.com/secdev/scapycdscapy
./run_scapyTo benefit from all Scapy features, such as plotting, you might want to install
Python modules, such asmatplotliborcryptography. See thedocumentationand
follow the instructions to install them.
Package: scapy2dict
# scapy2dictScapy packet to dictinstall-------`pip3 install scapy2dict`Usage-----```pythonfrom scapy.all import snifffrom scapy2dict import to_dictpkt = sniff(count=1)[0]# To ChainMap dictdata = to_dict(pkt)# to exactly dictdata = to_dict(pkt, strict=True)```
Package: sc.apyb.pythonbrasil6
sc.apyb.pythonbrasil6OverviewSite policy for the upcoming Brazilian Python Convention: PythonBrasil[7].RequirementsPlone 3.3.5 (http://plone.org/products/plone)InstallationTo enable this product,on a buildout based installation:Edit your buildout.cfg and addsc.apyb.pythonbrasil6to the list of eggs to install[buildout]
...
eggs =
    sc.apyb.pythonbrasil6After updating the configuration you need to run the ‘’bin/buildout’’,
which will take care of updating your system.Go to the ‘Site Setup’ page in the Plone interface and click on the
‘Add/Remove Products’ link.Choose the product (check its checkbox) and click the ‘Install’ button.Uninstall – This can be done from the same management screen, but only
if you installed it from the quick installer.Note: You may have to empty your browser cache and save your resource registries
in order to see the effects of the product installation.SponsoringDevelopment of this product was sponsored byAPyB.CreditsSimples Consultoria (products at simplesconsultoria dot com dot br) -
ImplementationChangelog2.0.2 (2011-06-09)Bumping version of Products.PyConBrasil [erico_andrei]2.0.1 (2011-06-06)Bumping version of theme package [erico_andrei]2.0.0 (2011-06-06)Adding new upgrade step [erico_andrei]Migrating files and images to blob storage [erico_andrei]Edicao Atual set to 2011 [erico_andrei]Changing navigation root to /2011 [erico_andrei]1.0.1 (2010-06-02)Added an upgrade step to change navigation_root for the site [erico_andrei]0.5 - UnreleasedInitial release
Package: scapy-man
Scapy Manager (scapy-man)Step CA Manager using PythonDocumenation:scapy.nikz.inSetupgitclonehttps://github.com/nikhiljohn10/scapycdscapy
pipinstallpoetry
poetryupdateDemopoetryrunbashexamples/deploy.shYou will be asked to provide aCloudflare Tokento upload the worker. The worker will be uploaded to your Cloudflare Account and deployed to Cloudflare Edge Network.If successully deployed, you will find a url where the demo CA Root certificate and hosted. Install this certificate in systems to accessstepca.nikz.in.Python Packagepipinstallscapy-manCompletionscapy--completionThis command will install the shell completion. To activate the shell completion in currently working shell, run. ~/.bash_completionorsource ~/.bash_completion.TroubleshootingIfscapynot found:export PATH=$PATH:$(realpath ~/.local/bin)Ifpipnot found:sudo apt install python3-pip -yIfpython3-pipnot found:sudo apt updateUsage with Step CAINSTALLATION_PATHS=$(scapygetstepall-p)sudodpkg-i$INSTALLATION_PATHSscapygenpasswords
scapygenworkerexportCA_NAME="Scapy CA"exportCA_DNS="$(hostname).local,localhost"stepcainit\--name"$CA_NAME"\--deployment-typestandalone\--dns"$CA_DNS"\--address":443"\--provisioneradmin\--password-file$(scapypathpasswordroot)\--provisioner-password-file$(scapypathpasswordprovisioner)stepcryptochange-pass$(scapypathkeyintermediate)-f\--password-file$(scapypathpasswordroot)\--new-password-file$(scapypathpasswordintermediate)exportFINGERPRINT=$(stepcertificatefingerprint$(scapypathcertroot))scapydeploy--workerscapy--jsworker.js

sudosetcapCAP_NET_BIND_SERVICE=+eip$(whichstep-ca)step-ca$(scapypathconfigca)--password-file$(scapypathpasswordintermediate)In the above commands,Generate a password and store in step pathGenerate a basic worker fileExportCA_NAMEvariable with CA NameGenerate PKI using Step CAExportFINGERPRINTvariable with fingerprint of Root CertificateDeploy workerscapywithworker.jsas script file.Commandline Interface
Package: scapy-packet-viewer
scapy-packet_viewerPacket viewer for SecDev'sScapy.InstallationInstall the latest release using pip (pip install scapy-packet_viewer) or manually from source by runningpip install .(preferred) orpython setup.py installin the cloned repository.scapy-packet_viewerby itself only installs the minimum required dependencies to run the bare packet viewer, but not any of its custom views. To install dependencies for those too, usescapy-packet_viewer[full]instead (or runpip install .[full]in the project root).
Package: scapy-unroot
scapy-unrootDaemon and tooling to enable usingscapywithout root permissions.Installationscapy_unrootcan be installed by just runningpipinstallThe requirements also installed by this are listed inrequirements.txt.UsageThescapy-unrootdaemonThe daemon to allow usage of scapy without root permissions requires root
itself. You can start it with the following command:sudoscapy-unrootscapyThe provided argumentscapyshould be a permission group, users who are
allowed to use scapy without root permissions should be in.By default, all files related toscapy_unrootare managed in the directory/var/run/scapy-unroot. You can change that directory using the-r/--run-dirargument:sudoscapy-unroot--run-dir/tmpscapyThe UNIX domain socket to communicate with the daemon will be created under the
nameserver-socketin that directory.Network interfaces that users ofscapy_unrootshould not be able to send over
or sniff on can be blacklisted using the-b/--interface-blacklistargument. Multiple interfaces can be provided:sudoscapy-unrootscapy--interface-blacklistwlan0eth0loTo run the daemon in background, use the-d/--daemonizeparameter:sudoscapy-unroot-dscapyTo get more information on the arguments of thescapy-unrootdaemon, runsudoscapy-unroot-hAll arguments described above can be combined.Configuring scapy to communicate with the daemonBefore sending or sniffing with scapy, just dofromscapy_unrootimportconfigure_socketsconfigure_sockets()You can provide a different server address by theserver_addrargument. The
default is/var/run/scapy_unroot/server-socket.You can also configure the timeout for waiting for a reply from the server using
theconnection_timeoutargument.
Package: scar
SCAR - Serverless Container-aware ARchitecturesSCAR is a framework to transparently execute containers out of Docker images in AWS Lambda, in order to run applications (see examples forImageMagick,FFmpegandAWS CLI, as well as deep learning frameworks such asTheanoandDarknet) and code in virtually any programming language (see examples forRuby,R,ErlangandElixir) on AWS Lambda.SCAR provides the benefits of AWS Lambda with the execution environment you decide, provided as a Docker image available in Docker Hub. It is probably the easiest, most convenient approach to run generic applications on AWS Lambda, as well as code in your favourite programming language, not only in those languages supported by AWS Lambda.SCAR also supports a High Throughput ComputingProgramming Modelto create highly-parallel event-driven file-processing serverless applications that execute on customized runtime environments provided by Docker containers run on AWS Lambda. The development of SCAR has been published in theFuture Generation Computer Systemsscientific journal.SCAR is integrated with API Gateway in order to expose an application via a highly-available HTTP-based REST API that supports both synchronous and asynchronous invocations. It is also integrated with AWS Batch. This way, AWS Lambda can be used to acommodate the execution of large bursts of short requests while long-running executions are delegated to AWS Batch.SCAR allows to create serverless workflows by combining functions that run on either AWS Batch or AWS Lambda which produce output files that trigger the execution of functions that, again, run on either AWS Batch or AWS Lambda, using the very same Docker images, thus effectively creating highly-scalable cross-services serverless workflows.Related resources:Scientific Paper(pre-print).Update 3.0.0Since version 3.0.0 SCAR creates a lambda layer called 'faas-supervisor' that includes the core functionality for the lambda containers.
This layer allows to deploy new functions faster. The layer is created once (the first time that a function is created or after a layer update) and then it's linked to all the other functions.If a new version of the supervisor is released (e.g. when a new feature is added or a bug is found) the functions can be updated with the commandscar update -a -sl.To check the supervisor layer version that your function is using you only have to do an ls likescar lsDocumentationSCAR documentation can be found inreadthedocs.Also the examples have extra information that is usefull to execute them.LicensingSCAR is licensed under the Apache License, Version 2.0. SeeLICENSEfor the full
license text.Further informationThere is further information on the architecture of SCAR and use cases in the scientific publication"Serverless computing for container-based architectures"(pre-print availablehere), included in the Future Generation Computer Systems journal. Please acknowledge the use of SCAR by including the following cite:A. Pérez, G. Moltó, M. Caballer, and A. Calatrava, “Serverless computing for container-based architectures,” Futur. Gener. Comput. Syst., vol. 83, pp. 50–59, Jun. 2018.Acknowledgementsudocker
Package: scArches
.. raw:: html|PyPI| |PyPIDownloads| |Docs|Single-cell architecture surgery (scArches) is a package for reference-based analysis of single-cell data.What is scArches?scArches allows your single-cell query data to be analyzed by integrating it into a reference atlas. By mapping your data into an integrated reference you can transfer cell-type annotation from reference to query, identify disease states by mapping to healthy atlas, and advanced applications such as imputing missing data modalities or spatial locations.Usage and installationSeehere <https://scarches.readthedocs.io/>_ for documentation and tutorials.Support and contributeIf you have a question or new architecture or a model that could be integrated into our pipeline, you can
post anissue <https://github.com/theislab/scarches/issues/new>__ or reach us byemail <mo.lotfollahi@gmail.com>_.ReferenceIf scArches is helpful in your research, please consider citing the followingpaper <https://www.nature.com/articles/s41587-021-01001-7>_:
::@article{lotfollahi2021mapping,
     title={Mapping single-cell data to reference atlases by transfer learning},
     author={Lotfollahi, Mohammad and Naghipourfar, Mohsen and Luecken, Malte D and Khajavi,
     Matin and B{\"u}ttner, Maren and Wagenstetter, Marco and Avsec, {\v{Z}}iga and Gayoso,
     Adam and Yosef, Nir and Interlandi, Marta and others},
     journal={Nature Biotechnology},
     pages={1--10},
     year={2021},
     publisher={Nature Publishing Group}}.. |PyPI| image::https://img.shields.io/pypi/v/scarches.svg:target:https://pypi.org/project/scarches.. |PyPIDownloads| image::https://pepy.tech/badge/scarches:target:https://pepy.tech/project/scarches.. |Docs| image::https://readthedocs.org/projects/scarches/badge/?version=latest:target:https://scarches.readthedocs.io
Package: scArchest
|PyPI| |PyPIDownloads| |Docs| |travis|scArches (PyTorch) - single-cell architecture surgery.. raw:: htmlThis is a Pytorch version of scArches which can be foundhere <https://github.com/theislab/scArches/>. scArches is a package to integrate newly produced single-cell datasets into integrated reference atlases. Our method can facilitate large collaborative projects with decentralise training and integration of multiple datasets by different groups. scArches is compatible withscanpy <https://scanpy.readthedocs.io/en/stable/>, and hosts efficient implementations of all conditional generative models for single-cell data.What can you do with scArches?Integrate many single-cell datasets and share the trained model and the data (if possible).Download a pre-trained model for your atlas of interest, update it with new datasets and share with your collaborators.Construct a customized reference by downloading a reference atlas, add a few  pre-trained adaptors (datasets) and project your own data in to this customized reference atlas.Project and integrate query datasets on the top of a reference and use latent representation for downstream tasks, e.g.: diff testing, clustering.Usage and installationSeehere <https://scarchest.readthedocs.io/>_ for documentation and tutorials.Support and contributeIf you have a question or new architecture or a model that could be integrated into our pipeline, you can
post anissue <https://github.com/theislab/scarchesp/issues/new>__ or reach us byemail <mailto:cottoneyejoe.server@gmail.com,mo.lotfollahi@gmail.com,mohsen.naghipourfar@gmail.com>_. Our package supports tf/keras now but pytorch version will be added very soon.ReferenceIf scArches is useful in your research, please consider citing thispreprint <https://www.biorxiv.org/content/10.1101/2020.07.16.205997v1/>_... |PyPI| image::https://img.shields.io/pypi/v/scarchest.svg:target:https://pypi.org/project/scarchesp.. |PyPIDownloads| image::https://pepy.tech/badge/scarchest:target:https://pepy.tech/project/scarchesp.. |Docs| image::https://readthedocs.org/projects/scarchest/badge/?version=latest:target:https://scarchesp.readthedocs.io.. |travis| image::https://travis-ci.com/theislab/scarchest.svg?branch=master:target:https://travis-ci.com/theislab/scarchesp
Package: scarecrow-data
ScarecrowScarecrow offers low code web development for data science.Demo[screenshots]Getting startedWhat is Scarecrow?Scarecrow offers a free and fully customisationbale framework for building data
science apps, which works out of the box with as much customisation as you need.
There are many frameworks for web development, some based on Python. There are
many frameworks and products for data analysis and visualisation, and some for
data science models. However, Scarecrow offers significant advantages over these
existing technologies:Python-based frameworks require additional learning: even if you understand
Python and its ecosystem, you still need to learn more about HTML, CSS, HTTP
requests, and routing infrastructure.Existing apps for data science that are written and run as a Python file
offer limited support for infrstructure like multiple pages, user
authentication, and session tracking.Existing no-code solutions for rapid web development offer limited access to
source code and personalisation, or they require significant membership fees.Is Scarecrow what you're looking for?Scarecrow is built in Python, HTML, CSS, and SQL. It draws on a range of
Python libraries - including those serving Flask functionality, and machine
learning modelling and visualisation with Sklearn, Tensorflow, Pandas,
Matplotlib, and Seaborn. At its core, a Scarecrow app it is built around a
Flask web application. This means HTTP routing determined by a Python app file,
leading to pages built on HTML templastes and formatted with CSS. It also
incorporates databases which are addressed through SQL.What else can you do with Scarecrow?As a user, you don't need to be an expert in any of the languages or libraries
used for Scarecrow, but you are free to customise your Scarecrow app if you know
how to use them. The base apps don't integrate and JavaScript, so they're simple
and predictable, but there's nothing stopping you from adding some JavaScript
elements into your Scarecrow app.Goals and future milestonesFurther documentation (links)How can you contribute?Scarecrow was established by Michael Hayman, who is fully responsible for its
development and maintenance. If you would like to contribute to Scarecrow,
please emailmdghayman@gmail.comto share your proposal and your background.LicenseCopyright (c) 2021 Michael Hayman, under the MIT License
Package: scared
SCAredscared is a side-channel analysis framework maintained byeShardteam.Getting startedRequirementsScared need python3.7,3.8,3.9or3.10.You can installscared, depending on your setup:from sourcewithpipwithcondaInstall withcondaYou just have to run:condainstall-ceshardscaredInstall withpipPython wheels are available from Pypi, just run:pipinstallscaredInstall from sourcesTo install from sources, you will need to run:pipinstall.from the source folder.If you are planning to contribute, seeCONTRIBUTING.mdto install the library in development mode and run the test suite.Make a first cool thingStart using scared by doing a cool thing:# First import the libimportscaredimportnumpyasnp# Define a selection function@scared.attack_selection_functiondeffirst_add_key(plaintext,guesses):res=np.empty((plaintext.shape[0],len(guesses),plaintext.shape[1]),dtype='uint8')fori,guessinenumerate(guesses):res[:,i,:]=np.bitwise_xor(plaintext,guess)returnres# Create an analysis CPAa=scared.CPAAttack(selection_function=first_add_key,model=scared.HammingWeight(),discriminant=scared.maxabs)# Load some traces, for example a dpa v2 subsetths=scared.traces.read_ths_from_ets_file('dpa_v2.ets')# Create a container for your thscontainer=scared.Container(ths)# Run!a.run(container)DocumentationTo go further and learn all about scared, please go tothe full documentation.
You can also have an interactive introduction to scared by launching thesenotebooks with Binder.ContributingAll contributions, starting with feedbacks, are welcomed.
Please readCONTRIBUTING.mdif you wish to contribute to the project.LicenseThis library is licensed under LGPL V3 license. See theLICENSEfile for details.It is mainly intended for non-commercial use, by academics, students or professional willing to learn the basics of side-channel analysis.If you wish to use this library in a commercial or industrial context,eShardprovides commercial licenses under fees.Contact us!AuthorsSeeAUTHORSfor the list of contributors to the project.Binary builds availableBinary builds (wheels on pypi and conda builds) are available for the following platforms and Python version.Platforms:Linux x86 64Macosx x86 64Python version:3.63.73.8
Package: scarfmatch
ScarfScarf is a python library for solving stable matching problems with couples, where one application is theNational Resident Matching Program. The package is named afterHerbert Scarf, the inventor of Scarf's lemma and algorithm.FeaturesWritten in python3.Solve for a near feasible stable matching given the preference of doctors and hospitals, and hospital capacities.Generate random doctor hospital instances for simulation purposes.Accelerated with Numba. It is fast!Accepted input format:Python ListsJSONAlgorithm:Scarf's algorithm with Iterative Rounding[Nguyen and Vohra 2016]Installationpip install scarfmatchExample Usageimport scarf

# An example with single doctors {s0, s1}, couples {c0=(c00, c01) c1=(c10, c11)}, and hospitals {h0, h1, h2} 
single_pref = [[0, 1, 2], # s0's preference on hospitals is h0 > h1 > h2 > unemployment
             [1, 0]] # s1's preference on hospitals is h1 > h0 > unemployment > h2
             # list only the hospitals preferred to unemployment 
couple_pref = [[(0, 0), (1, 1), (1, 0), (0, 1), (2, 2)], # c0's joint preference on hospital pairs
             [(1, 1), (2, 2), (1, -1)]] # c1's joint preference on joint plans for both members
             # for example, (1, 0) stands for "member 0 goes to hospital 1 and member 1 goes to hospital 0"
             # "-1" stands for the option of unemployment
             # list only the options preferred to (-1, -1) (i.e. both unemployed)
hospital_pref = [0, (0, 1), 1, (1, 0), (0, 0), (1, 1)] # all three hospitals have the same preference: s0 > c01 > s1 > c10 > c00 > c11 > vacancy
# If all hospital use the same preference, then hospital_pref is one list containing integers and tuples, where integers represent singles and tuples represent members of couples
# To specify different preference lists for different hospitals, please use a list of lists
# In each list, only specify acceptable doctors to the given hospital
hospital_cap = [2, 3, 1] # capacities of each hospital

S = scarf.create_instance(single_pref, couple_pref, hospital_pref, hospital_cap)
sol = scarf.solve(S) # solves for a possibly fractional stable matching
intsol = scarf.round(sol, S) # perform Nguyen and Vohra's IR algorithm to obtain a near feasible stable matching

print(intsol)For more examples involving JSON input please refer to the Jupyter Notebooks in the notebooks folder. For more ways to access the matching result, please refer to the docstring of scarf.ScarfSolution.SupportLicenseReleased under MIT licenseCopyright (c) 2019-2023 Dengwang Tang <dwtang@umich.edu>
Package: scarfs
ScarfsA library to find an approximate fixed point for a for a bounded vector valued
function.InstallationpipinstallscarfsUsageDefine the function you want to find a fixed point of using numba:fromnumbaimportnjit@njitdefroll(simp:np.ndarray)->np.ndarray:returnnp.roll(simp,1)For performance reasons, this function must be compiled by numba as a cfunc or
in nopython mode. Jitclass functions are currently not supported. The function
must also lie in a bounded space, three default spaces are provided: the
simplex, the simplotope, and the unit hypercube. If your bounded space is not
one of these, you'll need to first compute a homeomorphism between your space
and one of these. The main algorithm runs on the simplex, so you may find it
faster if you can project there directly.Once your function is defined, simply call one of the fixed point functions
with an initial position and a discretization:fromscarfsimportsimplex_fixed_pointsol=simplex_fixed_point(roll,np.array([1,0,0,0],float),100)The result is guaranteed to be within1 / discretizationof a true fixed
point (or a little larger for the other bounded spaces).Note that fixed points are difficult to approximate generally, so this may run
for a very long time.Also note that this library "trusts" you, so if you pass in invalid inputs, you
may get arcane errors.