Saravanan666/pypi-org huggingface dataset:
Package: pynetsys
PYNETSYSpynetsys is a collection of tools and malicious packets.Developed by Andrea Vaccaro from ANDRVV (c) 2022InstallingLinux, MacOS =pip3 install pynetsys --userWindows =pip install pynetsys --userToolsimportpynetsystrace=pynetsys.tool.traceroute("python.org")-->listprint(trace)arping=pynetsys.tool.arp()-->dictprint(arping)hostinfo=pynetsys.tool.hostlookup("python.org")-->dictprint(hostinfo)networks=pynetsys.tool.networkFinder()-->dictprint(hostinfo)----------------isonline=pynetsys.isOnline("python.org")-->boolprint(isonline)haswebprotocol=pynetsys.hasWebProtocol("http:\\python.org")-->boolORhaswebprotocol=pynetsys.hasWebProtocol("python.org")-->boolprint(haswebprotocol)hasSSLwebprotocol=pynetsys.hasSSLWebProtocol("https:\\python.org")-->boolORhasSSLwebprotocol=pynetsys.hasSSLWebProtocol("python.org")-->boolprint(hasSSLwebprotocol)addwebprotocol=pynetsys.addWebProtocol("python.org")-->strprint(addwebprotocol)removewebprotocol=pynetsys.removeWebProtocol("https:\\python.org")-->strORremovewebprotocol=pynetsys.removeWebProtocol("http:\\python.org")-->strprint(removewebprotocol)Create malicious packet and sendimportpynetsysPACKET,ID=pynetsys.packet.packet(target="example.org",attack=Packets.DEATH_PING)pynetsys.packet.start(PACKET,ID,_verbose=0)PACKET,ID=pynetsys.packet.packet(target="example.org",attack=Packets.SYN_FLOOD)pynetsys.packet.start(PACKET,ID,_verbose=0)PACKET,ID=pynetsys.packet.packet(target="aa:bb:cc:dd:ee:ff",attack=Packets.WIRELESS_DEAUTH)pynetsys.packet.start(PACKET,ID,_verbose=0)
Package: pynetviz
PyNetViz(Python Network Visualization)========A python network(graph) visualization with networkxIt works in IPython(Jupyter)https://github.com/koorukuroo/pynetvizWeb Demohttp://goo.gl/7U2IlVAcknowledgement========Ulsan National Institute of Science and TechnologySchool of Natural ScienceMathematical Scienceshttp://www.math.unist.ac.krInstalling----------.. code-block:: pythonpip install pynetvizUsage-----.. code-block:: pythonimport networkx as nximport pynetviz.sigmajs as nvsG = nx.Graph()G.add_edges_from([(1,2),(2,3),(3,4),(5,6)])G.add_edge(1, 3)G.add_edge(1, 6)nvs.make_gexf(G)nvs.make_html(drawEdges='true')nvs.view_html()Output:.. image:: http://i.imgur.com/i5fQyuJ.png:alt: Network(Graph) VisualizationLayout--------------------------.. code-block:: pythonG = nx.Graph()G.add_edges_from([(1,2),(2,3),(3,4),(5,6)])G.add_edge(1, 3)G.add_edge(1, 6)layout = nx.spring_layout(G)nvs.make_gexf(G, layout)nvs.make_html(drawEdges='true')nvs.view_html()Example--------------------------.. code-block:: pythonG = nx.karate_club_graph()layout = nx.spring_layout(G)nvs.make_gexf(G, layout, size=nx.degree_centrality(G))nvs.make_html(drawEdges='true')nvs.view_html(height=1000)* ``G`` Graph object of networkx.* ``layout`` Position nodes.* ``size`` Size nodes.* ``drawEdges`` True or False.* ``height`` Window height size.
Package: pynetworkd3
PyNetworkD3Create D3 visualization networks with PythonInstallationInstall usingpip!$pipinstallpynetworkd3Input JSON syntax{
    "nodes": [
        {
          "id": "id1",
          "attribute 1": "value attribute 1",
          "attribute 2": "value attribute 2",
          (...)
          "attribute N": "value attribute N",
        },
        {
          "id": "id2",
          "attribute 1": "value attribute 1",
          "attribute 2": "value attribute 2",
          (...)
          "attribute N": "value attribute N",
        },
        (...)
    ],
    "links": [
        {
            "source": "id1",
            "target": "id2",
            "attribute 1": "value attribute 1",
            "attribute 2": "value attribute 2",
            (...)
            "attribute N": "value attribute N",
        },
        (...)
    ]
}Every dictionary in "nodes" must have theidkey. The other keys are optionals.Every dictionary in "links" must have thesourceandtargetkey. The other keys are optionals. Also, each id in source and target must redirect to an existing node in "nodes".UsageTo use the library, import theGraphobject directly and use theexportmethod
to create a.htmlwith the visualization.fromPyNetworkD3importGraphdataset={"nodes":[{"id":1},{"id":2},{"id":3},{"id":4},{"id":5}],"links":[{"source":1,"target":3},{"source":2,"target":3},{"source":1,"target":3},{"source":5,"target":3},{"source":4,"target":1},]}graph=Graph(dataset,width=300,height=200,radio=10,tooltip=["id"])graph.export("output.html)Also you can write the instance in the last line of the notebook's cell (ckeck theexample in colab) to view the visualization.DevelopingThis library usesPyTestas the test suite runner, andPyLint,Flake8,Black,ISortandMyPyas linters. It also usesPoetryas the default package manager.The library includes aMakefilethat has every command you need to start developing. If you don't have it, installPoetryusing:makeget-poetryThen, create a virtualenv to use throughout the development process, using:makebuild-envActivate the virtualenv using:..venv/bin/activateDeactivate it using:deactivateTo add a new package, usePoetry:poetryadd<new-package>To run the linters, you can use:# The following commands auto-fix the codemakeblack!
makeisort!# The following commands just review the codemakeblack
makeisort
makeflake8
makemypy
makepylintTo run the tests, you can use:maketestsReleasingTo make a new release of the library,git switchto themasterbranch and execute:makebump!minorThe wordminorcan be replaced withpatchormajor, depending on the type of release. Thebump!command will bump the versions of the library, create a new branch, add and commit the changes. Then, justmergethat branch tomaster. Finally, execute amergeto thestablebranch. Make sure to update the version before merging intostable, asPyPiwill reject packages with duplicated versions.
Package: pynetworking
High level network communicationThis tool abstracts network communication to a level, where you don’t have to care about
network communication. Server side functions can be called at the client as they were local. Functions may be called
with parameters and may return values.NOTE:This library has currently not a stable version. You are welcome, to use this library in your project and
report issues or improvements.FeaturesDirectly call functions at the remote sideGet the return valuesDon’t care about socketsExampleWhen you have setup everything this is an example how easy it will
be to communicate between the server and client:At theserver:defadd(number1,number2):returnnumber1+number2To call it at theclient:result=server.add(5,10)print(result)# Output: 15More simple examples:https://github.com/JulianSobott/pynetworking/tree/master/pynetworking_examplesInstallationThe easiest way to install is to usepip:pip install pynetworkingIt is also possible to clone the repository fromGithubwith:git clone https://github.com/JulianSobott/pynetworking.gitDocumentationLatest stable documentation:https://pynetworking.readthedocs.io/en/latest/Or if you want the current documentation in a branch (e.g. dev), you can clone the repository,
open the cmd and cd to thedocsfolder. You needsphinxinstalled. Then
you can typemake htmland see the local created docs.Getting startedThere is agetting startedguide at
the documentation. In this guide you will learn how to write a simple login application. This guide covers all basics, that are necessary.If you are already familiar with this library and just need a brief recap, there is achecklistfor what you need in new projects.ContributeIssue Tracker:https://github.com/JulianSobott/pynetworking/issuesSource Code:https://github.com/JulianSobott/pynetworkingLicenseThe project is licensed under the Apache Software License.
Package: py-networking
Py-NetworkingNoticePy-Networking is at early stages of development. You are free to look around but we do not provide support at the moment
and the library can go throughout a significant changes and backward compatibility cannot be guaranteed.AboutPy-Networking (PN) is a python library that provides a new way to interact and configure your networking devices.
PN abstract the physical device and provides to the user an uniform API that does not depends from the specific device configuration or configuration syntax.
PN does not emulate features that device maybe missing but if two different devices implement a specific feature, this is exposed to the user in the same way.DocumentationDraft documentation is available athttp://docs.py-networking.comMailing ListUser’s mailing list can bebrowsedorpostedafter joining it.LicenseApache 2.0
Package: pynetworks
DescriptionA Python package that provides structure for networks of interconnected
nodes using theDOT
languagefor representation.InstallationThe source code forpynetworksis hosted onGitHub. You can installpynetworkswith pip:pipinstallpynetworksDocumentationRead the latest documentation forpynetworkshere.Example UsageNode>>>a=Node('A')>>>b=Node('B')>>>a.connect(b,3)Printingagives:graph {
    "A" -- "B" [label=3]
}shortest_path()>>>a=Node('A')>>>b=Node('B')>>>c=Node('C')>>>a.connect(b,3)>>>b.connect(c,5)>>>path=shortest_path(a,c)>>>path.weight8Printingpathgives:graph {
    "B" -- "C" [label=5]
    "A" -- "B" [label=3]
}Network>>>a=Node('A')>>>b=Node('B')>>>c=Node('C')>>>a.connect(b,3)>>>net=Network([a,b,c])Printingnetgives:graph {
    "C"
    "A" -- "B" [label=3]
}LicenseGNU.
Package: pynetworktables
RobotPy NetworkTables ProjectThis is a pure python implementation of the NetworkTables protocol, derived
from the wpilib ntcore C++ implementation. In FRC, the NetworkTables protocol
is used to pass non-Driver Station data to and from the robot across the network.This implementation is intended to be compatible with python 3.5 and later.
All commits to the repository are automatically tested on all supported python
versions using github actions.NoteNetworkTables is a protocol used for robot communication in the
FIRST Robotics Competition, and can be used to talk to
SmartDashboard/SFX. It does not have any security, and should never
be used on untrusted networks.NoteIf you require support for Python 2.7, use pynetworktables 2018.2.0DocumentationFor usage, detailed installation information, and other notes, please see
our documentation athttp://pynetworktables.readthedocs.ioDon’t understand this NetworkTables thing? Check out ourbasic overview of
NetworkTables.InstallationOn the RoboRIO, you don’t install this directly, but use the RobotPy installer
to install it on your RoboRIO, or it is installed by pip as part of the
pyfrc setup process.On something like a coprocessor, driver station, or laptop, make sure pip is
installed, connect to the internet, and install like so:pip install pynetworktablesSupportThe RobotPy project has a mailing list that you can send emails to for
support:robotpy@googlegroups.com. Keep in mind that the maintainers of
RobotPy projects are also members of FRC Teams and do this in their free
time.If you find a bug, please file a bug report using githubhttps://github.com/robotpy/pynetworktables/issues/newContributing new changesRobotPy is an open project that all members of the FIRST community can
easily and quickly contribute to. If you find a bug, or have an idea that you
think others can use:Fork this git repositoryto your github accountCreate your feature branch (git checkout -b my-new-feature)Commit your changes (git commit -am ‘Add some feature’)Push to the branch (git push -u origin my-new-feature)Create new Pull Request on githubAuthors & ContributorsDustin Spicuzza, FRC Team 1418/2423Peter Johnson, FRC Team 294
Package: pyneurgen
This package provides the Python “pyneurgen” module, which contains severalclasses for implementing grammatical evolution, a form of genetic
programming, and classes for neural networks.  These classes enable the
creation of hybrid models that can embody the strengths of grammatical
evolution and neural networks.While neural networks can be adept at solving non-linear problems, some
problems remain beyond reach.  For example, a difficult search space can
cause suboptimal solutions to be reached.  Also, multiobjective problems
become extremely difficult, if not impossible.  With genetic algorithms, a
more thorough search can be made.
Package: py-neuromodulation
py_neuromodulationAnalyzing neural data can be a troublesome, trial and error prone,
and beginner unfriendly process.py_neuromodulationallows using a simple
interface for extraction of established neurophysiological features and includes commonly applied pre -and postprocessing methods.Onlytime series datawith a correspondingsampling frequencyare required for feature extraction.The output will be apandas.DataFrameincluding different time-resolved computed features. Internally astreamget’s initialized,
which resembles anonlinedata-stream that can in theory also be be used with a hardware acquisition system.The following features are currently included:oscillatory: fft, stft or bandpass filtered band powertemporal waveform shapefooofmne_connectivity estimatesHjorth parameternon-linear dynamical estimatesvarious burst featuresline lengthand more…Find here the preprint ofpy_neuromodulationcalled“Invasive neurophysiology and whole brain connectomics for neural decoding in patients with brain implants”[1].The original intention for writing this toolbox was movement decoding from invasive brain signals[2].
The application however could be any neural decoding problem.py_neuromodulationoffers wrappers around common practice machine learning methods for efficient analysis.Find the documentation herehttp://py-neuromodulation.readthedocs.iofor example usage and parametrization.Installationpy_neuromodulation requires at least python 3.10. For installation you can use pip:pip install py-neuromodulationWe recommend however installing the package in a new new conda environment:git clone https://github.com/neuromodulation/py_neuromodulation.git
conda create -n pynm-test python=3.10
conda activate pynm-testThen install the packages listed in thepyproject.toml:pip install .Optionally the ipython kernel can be specified for the installed pynm-test conda environment:ipython kernel install --user --name=pynm-testThenpy_neuromodulationcan be imported via:import py_neuromodulation as py_nmBasic Usageimportpy_neuromodulationaspnimportnumpyasnpNUM_CHANNELS=5NUM_DATA=10000sfreq=1000# Hzfeature_freq=3# Hzdata=np.random.random([NUM_CHANNELS,NUM_DATA])stream=pn.Stream(sfreq=sfreq,data=data,sampling_rate_features_hz=sampling_rate_features_hz)features=stream.run()Check theUsageandFirst examplesfor further introduction.Contact informationFor any question or suggestion please find my contact
information atmy GitHub profile.References[1]Merk, T. et al.Invasive neurophysiology and whole brain connectomics for neural decoding in patients with brain implants,https://doi.org/10.21203/rs.3.rs-3212709/v1(2023).[2]Merk, T. et al.Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson’s disease. Elife 11, e75126,https://doi.org/10.7554/eLife.75126(2022).
Package: pyneuronjs
pyneuronjsPython utilities and middleware for neuron.jspyneuronjs analyzes the dependencies from user defined facades according
to the dependency tree, and outputs<script>tags and configurations
forneuron.jsThe dependency tree can be generated byneuron-package-dependencyInstall$pipinstallpyneuronjsUsagefrompyneuronjsimportNeuronn=Neuron(dependency_tree=dependency_tree,# must definedresolve=resolve,version=version,cache=cache,debug=False)pyneuronjscould be used either in template file or your python
controller.Hereis a sample Jinja2 template file.dependency_treedictthejson.loads()ed dependency
treeresolvefunction(id)=(optional) implements your own custom
resolver.resolveaccepts one parameteridwhich can be
either astrof module id or alistof module ids. If astris passed in, the method should returns the resolved absolute
url of the module id. Ifidis alist, an url of comboed
script files should be returned.debugfunction|bool=Falsetells pyneuronjs whether should
switch on debug mode. When on debug mode, no javascript files of
dependencies will be preloaded, and the output will not be
compressed.ifdebugis callable, pyneuronjs will use the return value of
methoddebugifdebugis a boolean value, anddebugis true, the debug
mode will be on.cachedict=Noneifcacheis defined, it should contains 3
methods:cache.has(key)returnsboolcache.get(key)looks up and returns the cached value by keycache.set(key, value)sets the value by keyversionstronly works ifcacheis defined. pyneuronjs
will usesversionto generate the key to cache the output resultmodule id<name>@<version><path>#forexample:'jquery@2.0.0/jquery.js'n.facade(entry, data=None)entrystrid of the entry moduleRegisters a facade entry withdata. pyneuronjs willReturns''(empty string), so you can use this method in python
templaten.combo(id…)Returns''n.css(id)Returns''n.output()Returnsstrn.output_css()ReturnsstrLicenseMIT
Package: pyneurons
Blessed be the name of the Lord our God, who reigns forever and ever. He is the Alpha and the Omega, the beginning and the end, the one who was, who is, and who is to come.He is our refuge and strength, a very present help in times of trouble. He is the creator of the heavens and the earth, the giver of life and the sustainer of all things.His love endures forever, and His mercy never fails. He is gracious and compassionate, slow to anger and abounding in love.He is our rock, our fortress, and our deliverer, in whom we take refuge. He is the light in our darkness, the source of our joy and the strength of our hearts.Let us give thanks to the Lord for He is good, His love endures forever. Let us sing praises to His name and make known His deeds among the nations. May the Lord be exalted and glorified forever and ever. Amen.—ChatGPT (March 2023)pyneurons
Package: pyneutralino
PyNeutralinoPyNeutralino is a little Python module for making simple HTML/JS GUI apps with Flask.PyNeutralino is based onNeutralinojs. So PyNeutralino inherits its lightweight and cross platform advantages. If you are familiar with Python and Flask, this would be a good solution for writing cross-platform applications.Installingpip install PyNeutralinoA Simple Example# app.pyfromflaskimportFlaskfrompyneutralinoimportPyNeutralinoapp=Flask(__name__)@app.route('/')defhello_world():return'Hello, World!'if__name__=="__main__":pn=PyNeutralino(app,name="myapp",port=9000)pn.width=1000pn.height=700pn.run()pythonapp.pySupportsPython2 >= 2.7Python3 >= 3.4PlatformmacOSLinuxWindows
Package: pyneva
Library for electricity meters Neva MTWorking via serial port or RFC2217 server.
IEC 61107 protocol is used (currently IEC 62056-21) and OBIS codes.Installationpython-mpipinstallpynevaUsage examplesCLI$python-mpynevaconnect-irfc2217://192.168.88.109:1884-mNevaMT324AOS--valactive_energycurrent--obis60.01.00*FF
Connectedto:[NEVAMT324.1106:11111111]Values:
total_energyActiveEnergy(total=16484.51,T1=12896.28,T2=3588.23,T3=0.0,T4=0.0)active_powerActivePower(phaseA=5.0,phaseB=1372.4,phaseC=122.3,total=1499.8)OBIS:60.01.00*FF60089784CodefrompynevaimportNevaMT324AOS# /dev/ttyX for linux local port# comX or COMX for Windows local portwithNevaMT324AOS("rfc2217://192.168.88.109:1884")asmeter:print(meter.model)# MT324 A OS 5(60)Aprint(meter.serial_number)# 11111111print(meter.active_energy)# ActiveEnergy(total=16348.84, T1=12790.08, T2=3558.76, T3=0.0, T4=0.0)print(meter.voltage)# Voltage(l1=233.81, l2=233.02, l3=232.15)print(meter.active_power)# ActivePower(l1=5.0, l2=1833.4, l3=130.3, total=1968.8)print(meter.seasonal_schedules)# (SeasonSchedule(month=1, day=1, weekday_skd_num=1, sat_skd_num=1, sun_skd_num=1),)# Returns tuple with seasonal schedules.# Each schedule specifies from which date the tariff starts,# and the numbers of tariff schedules on weekdays, Saturdays, Sundays separately.print(meter.special_days_schedules)# ()# Returns empty tuple if you have no special days tariff schedules (max 32 days).print(meter.tariff_schedules)# (#     TariffSchedule(parts=(#         TariffSchedulePart(hour=7, minute=0, T_num=1),#         TariffSchedulePart(hour=23, minute=0, T_num=2)#     )),# )# Returns tuple with tariff schedules.# Each tariff schedule contains parts of the schedule.# Each schedule part describes from what time of day the tariff starts# and tariff number (T1, T2, T3, T4).Supported modelsMT 324 A OS 5(60)A (NEVAMT324.11XX)MT 324 AR E4BS 5(60)A (NEVAMT324.25XX)
Package: pynewmarkdisp
Welcome to thepyNewmarkDisprepo and docspyNewmarkDispis an application software in Python to calculate permanent displacements in shallow earthquake-triggered landslides by the Newmark sliding block method.In this repo and in the packagedoumentationyou will find the source code, instructions for installation, docstrings, examples of use, development history track, and references.InstallationIt is suggested to create a virtual environment to install and use the program.Stable releaseWe recommend installingpyNewmarkDispfromPyPI, as it will always install the most recent stable release.  To do so, run this command in your terminal:pip install pynewmarkdispFrom sourcesThe sources forpyNewmarkDispcan be downloaded from theGithub repo. You can clone the public repository running the following command:git clone git://github.com/eamontoyaa/pynewmarkdispOnce you have a copy of the source code, you can install it with the following instruction:pip install -e .DependenciesThe code was written in Python 3.9. The packagesnumpy,matplotlib,scipy, andnumbaare required for usingpynewmarkdisp. They should be installed along with the package, however, all of them can also be manually installed from the PyPI repository by opening a terminal and typing the following code lines:pip install numpy==1.22.4
pip install matplotlib==3.7.1
pip install scipy==1.9.0
pip install numba==0.56.4CitationTo citepyNewmarkDispin publications, use the following temporary reference (until an associated article is published):Montoya-Araque, E. A., Montoya-Noguera, S., & Lopez-Caballero, F. (202X). An open-source application software for spatial prediction of permanent displacements in earthquake-induced landslides by the Newmark sliding block method: pyNewmarkDisp v0.1.0. url=https://github.com/eamontoyaa/pyNewmarkDispA BibTeX entry for LaTeX users is:@software{MontoyaAraque_etal_202X,author={Montoya-Araque, Exneyder A. and Montoya-Noguera, Silvana and Lopez-Caballero, Fernando},title={{An open-source application software for spatial prediction of permanent displacements in earthquake-induced landslides by the Newmark sliding block method: \texttt{pyNewmarkDisp}}},version={v0.1.0},url={https://github.com/eamontoyaa/pyNewmarkDisp},year={202X}}LicensepyNewmarkDispis a free and open-source application sorfware licensed under the terms of theMIT license. The following is a copy of the license:{!LICENSE!}
Package: pyNewsApi
pyNewsApi## This Package Returns Latest and top News Headlines from different sources of News by taking some argumentsInstallationRun the following to install$pip install pyNewsApiUsagefrom pyNewsApi import PYNEWSp = PYNEWS()Get News by country codesdata = p.get_headlines_by_country(countrycode='in') or p.get_headlines_by_country('in')print(data)Get News by news provider namedata = p.get_headline_by_source(source='techradar') or p.p.get_headline_by_source('techradar')print(data)Get News by category and country namedata = p.get_head_by_cat_country(cat='technology',country='in') or p.get_head_by_cat_country('technology','in')print(data)Get Top headlines by giving querydata = p.get_top_headlines_about(query='apple') or p.get_top_headlines_about('apple')print(data)Get all news from all sources no need to take any argumentsdata = p.get_from_all_sources()print(data)Get News by languagesdata = p.get_by_lang(lang='fr') or p.get_by_lang('fr')print(data)get help for the modulep.get_help()get all supported languagesp.display_languages()get country codesp.display_country_codes()get categoriesp.display_catcty()