Saravanan666/pypi-org huggingface dataset:
Package: ns1-python
NS1 Python SDKThis project is inactive development.A Python SDK for accessing NS1, the Data Driven DNS platform.AboutThis package provides a python SDK for accessing the NS1 DNS platform
and includes both a simple NS1 REST API wrapper as well as a higher level
interface for managing zones, records, data feeds, and more.
It supports synchronous and asynchronous transports.Both python 2.7 and 3.3+ are supported. Automated tests are currently run
against 2.7, 3.7, 3.8, 3.9 and 3.10.Installation$ pip install ns1-pythonDependenciesNone, but supports different transport backends. Currently supported:requests(synchronous, the
default if available)urllib (synchronous, the default if requests isn't available)twisted(asynchronous, requires 2.7 or 3.5+)Other transports are easy to add, seetransportExamplesSee theexamples directoryDocumentationIf you don't yet have an NS1 account,signup here (free)You'll need an API Key. To create one, login tothe portaland click on the Account button in the top right. Select Settings & Users, then
add a new API Key at the bottom.Documentation at ReadTheDocsNS1 REST API DocumentationTestsUnit tests usepytest(pip install pytest). 2.7 also requiresmockto be
installed (pip install mock).Tests should, of course, run and pass under python 2 and 3. We use tox to
automate test runs and virtualenv setup, seetox.inifor config.ContributionsPull Requests and issues are welcome. See theNS1 Contribution Guidelinesfor more
information.Editing the docsYou can create or edit NS1-python documentation by downloading the repo onto your machine and using an editor such as VSCode.Creating Pull RequestsWhen you're ready to submit your changes, add a descriptive title and comments to summarize the changes made.SelectCreate a new branch for this commit and start a pull request.Check thePropose file changebutton.Scroll down to compare changes with the original document.SelectCreate pull request.Our CI process will lint and check for formatting issues withflake8andblack.
It is suggested to run these checks prior to submitting a pull request and fix
any issues:pip install flake8 black
flake8 . --count --show-source --statistics --extend-ignore=E501
black . --check -l 79 --diff
Package: ns2json
NS2JSONName Server to JavaScript Object Notation is a Python script to converts and saves a data from a DNS response to a JSON file.InstallingInstall the module bypip(checkhow to use pip):py -m pip install ns2jsonHow to usepy-mns2json[domain][dns][path]DomainDNSPathFull nameDomain nameDNS resolverPath to saveRequiredYesNoNoDefaultNo1.1.1.1From executed scriptExamplegoogle.com8.8.8.8C:\Users\Name\DesktopExamplespy-mns2jsongoogle.com8.8.8.8C:\Users\Name\Desktoppy-mns2jsongoogle.com8.8.8.8py-mns2jsongoogle.comC:\Users\Name\Desktop# Output file name: google.com.jsonCommentsThe script uses the nslookup tool.Currently, the script works only on Windows.To DoLinux version.
Package: ns4600
The Promise SmartStor NS4600 is great.  But have you ever wanted to remotely
start it up and shut it down without having to touch the web UI? Well, now you
can do just that with this module.UsageControl your NS4600 with ease:import ns4600

# wake up NAS (must enable Wake-On-LAN on device)
ns4600.wake('00:01:02:03:04:05')
    ...

# shut down NAS
ns4600.shutdown('admin','password','http://my-ns4600.local')InstallationInstalling the ns4600 module is simple:$ pip install ns4600
Package: n-sam
Numeric Safe Action Model Learning FrameworkSAM learning algorithm for numeric action model learning.InstallationThe following explains how to install the SAM-Learning framework.
Notice, the code works on linux machines due to its dependencies on planners and validators that only work on linux machines.Needed external tools:ENHSP solver- can be downloaded fromhere.
2.Note- for this code we require the binary code of the latest version of ENHSP (20 currently) with the jar file.Metric-FF planner- can be downloaded fromhere. In this algorithm, we use version 2.1 of Metric-FF which is the latest version.VAL validation algorithm- can be downloaded fromhere. Use the instructions given in the repository to install VAL.Environment variables'METRIC_FF_DIRECTORY': Directory of the compiled metric-FF planner.<path to containing directory>/Metric-FF-v2.1/'ENHSP_FILE_PATH': Path to the compiled ENHSP planner (the jar file).<path to containing directory>/enhsp.jar'CONVEX_HULL_ERROR_PATH': Path to the file that will contain the errors raised from the convex hull algorithm. Used for future debugging purposes.<path to containing directory>/convex_hull_error.txt'VALIDATOR_DIRECTORY': Directory containing VAL compiled code.<path to containing directory>/VAL/Code initializationClone the content of the repository fromGitHub.Install the dependencies usingpip install -r requirements.txt.Set the required environment variables.Run the tests usingpyteston the code to validate the installation.UsageTo run the algorithm end-to-end there are three stages:trajectory creation.model learning.validation.The first stage is the trajectory creation stage, which is independent of the latter stages.
The second and third stages are dependent on the first stage since the process will not run without trajectories.
The algorithm returns a safe action model that can be validated using the VAL algorithm.Create a directory containing thedomain file, theproblemsand, in case that the tested domain is numeric, a mapping file that contains a mapping of the preconditions to their respected fluents.
For example -sattelite_fluents.json.{
    "turn_to": ["(fuel ?s)", "(slew_time ?d_new ?d_prev)"],
    "switch_on": [],
    "switch_off": [],
    "calibrate": [],
    "take_image": ["(data_capacity ?s)", "(data ?d ?m)"]
}LicenseThis work is licensed under the MIT License.
Package: nsaphx
nsaphx is a Python package designed for causal inference studies under the potential outcome framework. It provides a flexible and extensible framework for defining and applying computational instructions to input data, which should include outcome, exposure, and confounders. The package uses a directed acyclic graph and database storage to ensure efficient computation and storage of each object. Instruction handlers can be easily extended by defining new classes and methods, which can then be used to create new instructions that can be applied to data. Each object is computed only once and stored in the database, ensuring that computation is efficient and data is not duplicated.InstallationPyPIpipinstallnsaphxSourcePlease note that the package requires Python 3.7 or higher.gitclonehttps://github.com/NSAPH-Software/nsaphxcdnsaphx
pipinstall.
Package: nsapi
Query the Dutch railways about your routes, getting info on delays and
more. See below for the syntax and example output.InstallationFrom PyPIAssuming you already are inside a virtualenv:pipinstallnsapiFrom GitCreate a new virtualenv (if you are not already in one) and install the
necessary packages:gitclonehttps://github.com/aquatix/ns-api.gitcdns-apimkvirtualenvns-apipipinstall-rrequirements.txtDevelopment in DockerGit clone this repository, use vscode & docker to remote open in a dev containeras explained in this howto.As part of ns-notificationsAlternatively, follow the installation instructions ofns-notifications,
which makes extensive use of this library to serve notifications to for
example a smartphone. The requirements of both packages can be installed
in the samens-notificationsone mentioned in the project;ns-apiwill be installed through pip fromPyPI.Home AssistantThe very usefulHome Assistanthome automation softwareincludes an integration for the ns-api. Be sure to check it out.Also take a look at nsmapsBart Römgens createda fascinating contour map called nsmapsbased on ns-api. It visualises Dutch railways travel information with OpenLayer 3 contour maps to show how long it takes to get somewhere in the Netherlands by train and bicycle.Example applicationFor example, I use the library to push notifications about my route to
my phone throughPushbullet. The program I
use to do this has its own repository:ns-notifications.NS API keyTo actually be able to query theNederlandse Spoorwegen
API, youneed to subscribe. This immediately
gives you a primary and secundary key you need for access.The library uses thereisinformatie API. Here you can try the API for yourself (and look up station names, but the library has the get_stations() function for this too).The API portal alsoprovides statistics on usage and errors.
Package: ns-api
NS APIUnofficial wrapper for the Dutch railwayAPI(Nederlandse Spoorwegen). The NS API currently features the following services:PricesCurrent departure timesDisruptions and engineering workThe station list, with all stations in the Netherlands including GeodataTravel recommendations from station to stationfromnsapiimportNSAPIns=NSAPI('yourkey')ns.get_all_stations()fromnsapiimportAsyncNSAPIasyncdefexample():asyncwithAsyncNSAPI('yourkey')asns:awaitns.get_all_stations()LicenseLicensed under theMIT License.
Package: nsaproxy
nsaproxy projectQuickstartUsing nsaproxy in Dockerdocker-compose up -dSeedocker-compose.ymlInstallationpip install nsaproxyRunningRunning daemonnsaproxy -c <conffile> -p <pidfile> --logfile <logfile>Running foregroundnsaproxy -f -c <conffile> -p <pidfile> --logfile <logfile>
Package: ns-asphalt9
ns_asphalt9ns-asphalt9是一个基于图像采集和蓝牙模拟手柄自动完成Asphalt9日常任务的工具。目前支持的功能如下：模式设置- 支持多人一、多人二、多人三、野兽年任务设置- 支持在循环模式中执行定时任务，支持的任务有免费抽卡、大奖赛抽卡、寻车、传奇寻车、自动重启、商店刷新通知自动选车- 多人一可以根据段位选车，可以配置不同段位所使用的车库等级以及所使用的车辆自动扫描寻车位置- 由于每日任务经常有新活动上架或者老活动下架导致寻车位置经常变动，当变动以后会自动扫描活动查找寻车活动位置自动选路- 根据地图自动选择路线错误重进- 支持识别系统错误、连接错误、服务错误等发生以后重新进入游戏log追踪- 完善log体系，可以追溯任何操作这个工具是基于nxbt控制switch、v4l2图像采集、tesseract-ocr5识别图像开发的。因此使用前需要准备一些硬件并搭建一下环境。推荐使用的系统环境为windowslinuxmac(全平台)NS狂野飙车9工具交流、历史兑换码、UnEdge前100俱乐部申请等请加vx: unedge或qq: 77686805硬件依赖硬件连线图参考如下:HDMI采集卡(必选)- 用于switch画面输入到虚拟机USB蓝牙(必选)- 用于模拟手柄向switch发消息,Linux环境下可用本机蓝牙，Mac，Windows，群晖虚拟机请买usb蓝牙，注意群晖请需要适用于群晖的免驱蓝牙。HDMI分配器一分二 一进二出(可选)- 同时输出switch到显示器和采集卡，方便查看工具操作以及在工具和手柄间自有切换。详细教程Wiki免责声明ns_asphalt为python学习交流的开源非营利项目，仅作为程序员之间相互学交流之用，使用需严格遵守开源许可协议。严禁用于商业用途，禁止使用ns_asphalt进行任何盈利活动。对一切非法使用所产生的后果，我们概不负责。许可证广告用户交流QQ群:A9 Auto使用 & 狂野飙车9交流群微信交流群: 请加unedge
Package: nsbl
nsblAnsible environment generator.DescriptionDocumentation still to be done.DevelopmentAssuming you usepyenvandpyenv-virtualenvfor development, here's how to setup a 'nsbl' development environment manually:pyenv install 3.7.3
pyenv virtualenv 3.7.3 nsbl
git clone https://gitlab.com/frkl/nsbl
cd <nsbl_dir>
pyenv local nsbl
pip install -e .[develop,testing,docs]
pre-commit installCopyright & licenseParity Public License 6.0.0Please check theLICENSEfile in this repository (it's a short license!), also check out thefreckleslicense pagefor more details.frkl product idsVersions:0.9:97de2bf5-0fbb-4884-9d26-488217e1477c1.x.x:97de2bf5-0fbb-4884-9d26-488217e1477cCopyright (c) 2019 frkl OÜ
Package: nsb-ml-py-pkg
MyML Project:Project name: MyMLThis project actually defines the ML project structure.Descriptioncreate virtual environment:conda create --name venv python=3.9activate the venvconda activate venvcreate a project foldermkdir MyMLdo some following installationedit conf.py file:
>import os
>import sys
>sys.path.insert(0, os.path.abspath("../my_py_pkg/src"))
>project = 'MyML'
>copyright = '2023, Khandoker Nosiba Arifin'
>author = 'Khandoker Nosiba Arifin'
>release = '0.0.0.1'
>extensions = [
"sphinx.ext.viewcode",
"sphinx.ext.todo",
"sphinx.ext.autodoc"
]
>templates_path = ['_templates']Add modules into the insert.rstdo some following installation 3 & 4using cmd
>cd docs
>sphinx-quickstart
>cd my-py-pkg
>sphinx_apidoc -o ../docs src/
>cd docs
>make html
9. If you want to show the html file then go to the --> docs>build>html>index.html the right click an copy the file and open it using the browser.
10. then create setup.py and install the following
11. then for build and upload package to the pypi.org and we must have an account to the pypi.org. note: package name must be unique otherwise we will get error.python setup.py sdist bdist_wheel
twine upload dist/*
> username:token> password: "your-api-token"Installationinstall conda(miniconda3)install make (for running make file)
After installing makemake --version
make install (for running the Makefile and installing the packages which are include into the requirements.txt)
(we have to create docs folder where all the documentations of our project are being saved now we have to install these 3 & 4 no)pip install sphinxpip install sphinx-rtd-theme(for setup.py)
5. pip install setuptools
(for locally publish the package)
6. pip install -e .
7. pip install twineContributorsLicenseGo to thehttps://opensource.org/> Licenses > popular/ strong Community> MIT License(copy & paste to the LICENSE file & edit the author name and copyright year)
Package: nsc
Nonlinear-Statistical-CouplingLibrary of core functions for the statistics of nonlinear systems.Methods were originally described in two papers [1,2] by Nelson and Umarov.Subsequent developments [3-5] refined the definition of nonlinear statistical coupling to be the inverse of the degree of freedom and strength of fluctuations       defined by the relative variance of an expontial distribution with fluctuations in the standard deviation defined by a gamma distribution.Applications include a variety of Complex Decision Systems including information fusion [6-8], machine learning [9, 10], and generalized information metrics [11, 12]References[1] K. P. Nelson and S. Umarov, “The relationship between Tsallis statistics, the Fourier transform, and nonlinear coupling,” arXiv:0811.3777v1 [cs.IT], 2008.[2] K. P. Nelson and S. Umarov, “Nonlinear statistical coupling,” Phys. A Stat. Mech. its Appl., vol. 389, no. 11, pp. 2157–2163, Jun. 2010.[3] K. P. Nelson, “A definition of the coupled-product for multivariate coupled-exponentials,” Phys. A Stat. Mech. its Appl., vol. 422, pp. 187–192, Mar. 2015.[4] K. P. Nelson, S. R. Umarov, and M. A. Kon, “On the average uncertainty for systems with nonlinear coupling,” Phys. A Stat. Mech. its Appl., vol. 468, pp. 30–43, 2017.[5] K. P. Nelson, M. A. Kon, and S. R. Umarov, “Use of the geometric mean as a statistic for the scale of the coupled Gaussian distributions,” Physica A, vol. 515, pp. 248–257, 2019.[6] K. P. Nelson, B. J. Scannell, and H. Landau, “A Risk Profile for Information Fusion Algorithms,” Entropy, vol. 13, no. 8, pp. 1518–1532, 2011.[7] K. P. Nelson, B. J. Scannell, and H. Landau. "Risk management for object identification." U.S. Patent No. 8,595,177. 26 Nov. 2013.[8] K. P. Nelson, M. Barbu, and B. J. Scannell, “Probabilistic graphs using coupled random variables,” in SPIE Sensing Technology & Applications, 2014, p. 911903.[9] S. Cao, J. Li, K. P. Nelson, and M. A. Kon, “Coupled VAE: Improved Accuracy and Robustness of a Variational Autoencoder,” arXiv:1906.00536[cs.LG], vol. 1, pp. 1–19, Jun. 2019.[10] C. A. George, E. A. Barrera, and K. P. Nelson, “Applying the Decisiveness and Robustness Metrics to Convolutional Neural Networks,” arXiv:2006.00058[cs.LG], May 2020.[11] K. P. Nelson, “Reduced Perplexity: A simplified perspective on assessing probabilistic forecasts,” in Advances in Info-Metrics, M. Chen, J. M. Dunn, A. Golan, and A. Ullah, Eds. Oxford University Press, 2020.[12] K. P. Nelson, “Assessing Probabilistic Inference by Comparing the Generalized Mean of the Model and Source Probabilities,” Entropy, vol. 19, no. 6, p. 286, Jun. 2017.
Package: nscmd
nscmdA terminal user interface (TUI) Python library utilizing inheritance to create namespaces for command execution. The project stemmed from a large refactoring of the cmd library, and eventually led to building an entirely separate library for the sake of design and code cleanliness.This library currently is only targeted at GNU/Linux platforms due to its use of the readline library for several major features.FeaturesNamespacesBuilt-in help via docstringsCommand historyTab completeClear terminalMulti-file interpreterExampleIn a file calledbar.py:fromfooimportFooInterpreterclassBarInterpreter(FooInterpreter):name='bar'defdo_helloworld(self,args):return"Hello, bar!"In a file calledfoo.py:importnscmdclassFooInterpreter(nscmd.SubInterpreter):name='foo'defdo_helloworld(self,args):"""prints Hello, foo!"""return"Hello, foo!"if__name__=="__main__":# JIT imports allow for multi-file interpretersfrombarimportBarInterpreter# run commands from a list...cmds=["main foo","helloworld","main foo bar helloworld"]m=nscmd.MainInterpreter(cmd_in=cmds)m.run()# ... and get the results in a list!print(nscmd.outqueue)# ... or use files for input and outputm=nscmd.MainInterpreter(cmd_in="example_cmds.txt",outfile="example_output.txt")m.run()# Use a string!cmdstr="main foo\nhelloworld\nmain foo bar helloworld\n"m=nscmd.MainInterpreter(cmd_in=cmdstr)m.run()print(nscmd.outqueue)# or run as a standard TUIm=nscmd.MainInterpreter()m.tui()# no matter what method you use, you can still access# the output as a list. It resets on each instantiation.print(nscmd.outqueue)
Package: nscrap
nscrap주기적으로 Scraper 를 실행하여 기사제목에 특정 키워드가 포함되면 Messenger 를 통해 알림을 발송합니다Installationpip install nscrapOrgit clone https://github.com/pparkddo/nscrap.git
pip install -r requirements.txtUsagefromdatetimeimportdatetimeimportrequestsfrombs4importBeautifulSoupfromnscrap.scraperimportArticleScraper,ArticleConnectionError,ArticleParsingError,Articlefromnscrap.runnerimportScraperRunnerfromnscrap.messengerimportTelegramMessengerfromnscrap.pressimportPressfromnscrap.keywordsimportKeyword# Define ScraperclassHkScraper(ArticleScraper):def__init__(self):self.url="https://www.hankyung.com/all-news/"defget_press_name(self):return"한국경제"defget_articles(self):response=self._get_response()parsed=self._parse_articles(response)return[Article(title=each.text,link=each["href"],timestamp=datetime.now())foreachinparsed]def_get_response(self):try:returnrequests.get(self.url)exceptExceptionaserr:raiseArticleConnectionError(f"{self.get_press_name()}scraper requests error")fromerrdef_parse_articles(self,response):try:soup=BeautifulSoup(response.text,"html.parser")title_classes=soup.find_all(class_="tit")return[title_class.find("a")fortitle_classintitle_classes]exceptExceptionaserr:raiseArticleParsingError(f"{self.get_press_name()}scraper parsing error")fromerr# 'telegram-bot-token': 텔레그램에서 발행한 봇토큰을 입력# 123456789: 메시지를 보낼 대화방 IDmessenger=TelegramMessenger("telegram-bot-token",123456789)press=[Press("한국경제",True,30),]keywords=[Keyword("백신"),Keyword("정부"),]scrapers=[HkScraper(),]runner=ScraperRunner(messenger)runner.add_press(press)runner.add_keyword(keywords)runner.add_scraper(scrapers)runner.start()# ctrl+c 를 입력하면 스케쥴러 종료Output[+] Send validation message from nscrap
[+] Test succeed: 한국경제 passed test
press: [Press(press_name='한국경제', active=True, delay=30)]
keywords: ['정부', '백신']
scrapers: ['한국경제']
[+] Start nscrap
[+] Start 한국경제 scraper at 2020-12-12 23:23:12
[+] Scrap 기사제목(https://www.hankyung.com/기사링크)
[+] Start 한국경제 scraper at 2020-12-12 23:23:42
...
[+] Stop nscrapCustomizationnscrap.scraper.ArticleScraper 를 상속하여 여러 scraper 구현 가능nscrap.messenger.Messenger 를 상속하여 다양한 메신저 구현가능nscrap.container.ArticleContainer 를 상속하여 ArticleRunner 내부에서 사용할 article 저장소 구현가능
Package: nscrapy
NSCRAPY: Scraper de principales portales de noticias argentinos.Libreria para bajar notas de clarin , pagina12, cronica y cronista.Instalacion:pipinstallnscrapyClases para scrapear una url de una nota.clarin()p12()cronica()cronista()Ejemplo de uso, scrapear el portal de clarin de hoy (para pagina 12 cambiar clarin por p12) con comentarios:fromnscrapyimportnscrapasnsfromnscrapy.clarinimportgetnotasclarin=ns.clarin()#scrapeo el portalnotasclarin.hoy()#las urls a las notas de hoy estan en .urlsnotashoy=get(notasclarin.urls)notashoy es una lista de objetos nota:nota.titulo:titulodelanotanota.comm:comentariosnota.com:' '.join(comentarios)nota.volanta:volantadelanotanota.bajada:resumenosubtitulonota.cuerpo:cuerponotanota.bolds:textosennegritanota.bold:' '.join(bolds)
Package: nsdcode
nsdcodeThis repository contains nsd_mapdata.{m,py}, a light-weight utility that
allows the user to map data between different reference spaces
(e.g. anatomical, functional, volume-based, surface-based) in the NSD dataset.
Both MATLAB and Python versions of the utility are provided.For more information on the NSD dataset, please seehttp://naturalscenesdataset.org.For examples of how to use nsd_mapdata, please take a look at examples_nsdmapdata.{m,py}.Known issues:Note that the MATLAB and Python implementations give extremely similar but not
numerically identical results due to differences in interpolation implementation.
Also, voxels near the edges of valid locations are also handled slightly differently
in the Python version.The Python version does not yet implement the anat-to-anat case.MATLABThe MATLAB implementation relies on a few external toolboxes that are provided
in the matlab/external directory.To install, unzip the matlab/external/*.zip files in place, and thenaddpath(genpath('/path/to/nsdcode/matlab/'));Because the utility relies on transformation files provided with the NSD dataset,
the user must editnsd_datalocation.mwith the location of your local copy
of the NSD dataset.PythonTo install:cdpython
pythonsetup.pydevelopCode dependencies:There are some external dependencies which are listed in requirements.txt
These are installed automatically when you run the setup above.
These dependencies include:nibabelscipynumpytqdmChange historyVersion 1.0 (Dec 20, 2020). This is the first official release of the software.
Package: nsdf
nsdfNSDF (Neuroscience Simulation Data Format) is a file format built
on top ofHDF5.Although the design and development started with the aim of storing
data generated from simulations in computational neuroscience, this
format is generic enough that any time series data should fit in. Thus
the actual application can be much broader than simulations in
neuroscience.If you use nsdf for your research, please consider citing the article with NSDF specification:Ray, Subhasis, Chaitanya Chintaluri, Upinder S. Bhalla, and Daniel K. WÃ³jcik. 2015. "NSDF: Neuroscience Simulation Data Format." Neuroinformatics, November, 1â€“21. doi:10.1007/s12021-015-9282-5.RequirementsThe latest nsdf module works with h5py 3x, Python 3 and numpy.To build the documentation you also need sphinx,
sphinxcontrib-napoleon packages.InstallationTo install from PyPI use:If you have admin rights on a linux and want to install it for all users:sudo pip install nsdfIf you don't have admin rights or want to install in your home directorypip install nsdf --userTo install the nsdf package from the source (available at
"https://github.com/nsdf/nsdf"):Open a terminal, cd to the top level directory (the one containing
this file) and enter:python setup.py installTo build the documentation, cd to doc directory and enter:make htmlThis will create "_build" directory and the index of the docs will be
in "_build/html/index.html". Or you can read the latest documentation
built from git:master branch on nsdf.readthedocs.org.Other ToolsdatavizA GUI utility for viewing HDF5 datasets.Moogliis a sister project of theMOOSEneuro-simulator.
It is a simulator independent visualization tool for neuronal simulations.CreditsSubhasis Ray, Chaitanya Chintaluri, Upinder Bhalla and Daniel WÃ³jcik
have been designing the specification in collaboration.Chaitanya is providing examples and use cases.Subhasis is developing this Python module providing a high level API
for reading and writing NSDF files.We thank Aviral Goel, Johannes Rieke and Matteo Cantarelli for their
critical input.Zbigniew JÄ™drzejewski-Szmek contributed to structuring for packaging.