Saravanan666/pypi-org huggingface dataset:
Package: next-review
Start your next gerrit code review without any hassle.So you have 10 minutes to spend on code reviews and you want to be as
productive as possible. You definitely don’t want to spend 9 minutes shuffling
through code reviews that Jenkins already hates, you’ve already reviewed, etc.,
and you should definitely be looking at that awesome patch that’s about to
expire due to two weeks of inactivity.Solution: Usenext-reviewto immediately jump to the “highest priority”
code review currently awaiting your gracious downvotes. Inhale some code,
articulate your opinion, cast your vote and then move on to yournext-review. Got it?InstallationFrom PyPi:$ pip install next-reviewUsageIf you can usegit-review, you can probably usenext-review. Assuming
you’re watching some projects in gerrit, have an SSH key public key somewhere
obvious and your login name matches your gerrit username, you can just do:$ next-review
https://review.openstack.org/88443 stackforge/python-openstacksdk Add Transport docThe link will be automatically opened for you, because that’s how lazy I am.You can also abuse the return code to see how many reviews you have left to go
until it’s time for beer and/or sleep:$ echo $?
5Or, you can just view the entire list without automatically opening any links:$ next-review --list
https://review.openstack.org/88443 stackforge/python-openstacksdk Add Transport doc
https://review.openstack.org/85210 openstack/keystone Fix variable passed to driver module
https://review.openstack.org/89458 openstack/python-keystoneclient Make auth_token return a V2 Catalog
https://review.openstack.org/90943 openstack/keystone Refactor create_trust for readability
https://review.openstack.org/91440 openstack/identity-api Replace non-breaking spaceConfiguration Filenext-reviewhas the concept of a multi-section (ini-style) configuration
file.  The default location it looks for it is~/.next_review.  In this
configuration file the default section is[DEFAULT]and the following
options are supported:host,port,username,email,key,
andprojects.  These values will override the defaults, but any
cli-arguments that are explicitly set will take precedence over the config
file.If you specify sections other than[DEFAULT]you can use the--config-sectionargument to specify the section that should be used.  If a given option does not
exist in the specified section, the parser will look in[DEFAULT]and if
the option does not exist in either section, it will fall back to the global
defaults.  So the order of precedence would be option passed on the command
line, options in the section specified by the--config-sectionargument,
options in the[DEFAULT]section, and finally the global defaults.PhilosophyOlder changes should be reviewed first.If Jenkins is failing a change, then the author has work to do.If SmokeStack is failing a change, then the author has work to do. If
SmokeStack hasn’t reviewed a change, that’s okay… SmokeStack is lazy, too.If a change is already blocked by a core reviewer or marked WIP or Draft,
then it’s not going to merge right now anyway.
Package: nextsms
nextsmsPython Package to easy the integration with nextsms SMS GatewayGetting startedIn order to use this package you need to have the NextSMS Account, If you don't have one please take a look atNextSMS, and get yourusernameandpasswordwe gonna use them down below;Install the libary from pip or directlypipinstallnextsmsInstalling directly from githubClone this repository or Download a zip of the package  and then do this;gitclonehttps://github.com/Kalebu/nextsmscdnextsmsnextsms->pythonsetup.pyinstallHere is an example on how to send an SMS with this package;>>importnextsms>>sender=nextsms('your username','your password')>>sender.sendsms(message='hello',recipients=['25575XXXXX','25565XXXX'],sender_id="NEXTSMS")Bulky ?NextSms allows you to predefine all message you would like to send with variety of sender IDs and does the rest on how to successful send to all.Here an Example how you would do that with this package>>importnextsms>>sender=nextsms('KalebuJordan','kalebu@opensource')>>messages=[{'from':'NEXTSMS','to':'255757294146','text':'hello'},{'from':'NEXTSMS','to':'255754205561','text':'hello'}]>>sender.send_bulk(messages)EnvironmentBy default as you can create a nextsms instance it configure the environment to use production urls, If you wanna do on test sandbox here how;>>importnextsms>>sender=nextsms('KalebuJordan','kalebu@opensource')>>sender.sandbox=TrueWanna Contribute ?JustFork itCreate a new branchDo your changesMake a Pull requestYou're merged congrats .!!!Give it a starWas this useful to you ? Cool then give it a big star !Connect with meI use a lot of twitter ,let's connect on twitter.IssuesEncountered issues while using the package, raise an issue and then we gonna work to fix it as soon as it takesMorefeaturescomingsoonAll the Credits tokalebu
Package: nextstep
nextstepIntroductionNextstep integrates major popular machine learning algorithms, offering data scientists an all-in-one package. At the same time, it lifts the programming constraints by extracting key hyper-parameters into a configuration dictionary, empowering less experienced python users the ability to explore machine learning.Nextstep was originally developed for a data science challenge which involved price prediction. So it has a dedicated module to obtain data (oil and weather) via open API or web-scraping. It evolves into a machine learning prediction toolkit.InstallationFirst time installationpipinstallnextstepUpgrade to the latest versionpipinstallnextstep--upgradeQuick TutorialgetData modulegenerate oil pricesfromnextstep.getData.oilimport*oil_prices.process()brent_daily.csvandwti_daily.csvwill be generated at the current directory. They contain historical oil price until the most recent day.generate weather dataThis function relies on an API key fromworldweatheronline. It is free for 60 days as of 27/3/2020. It will generate csv data files in the current directory.fromnextstep.getData.weatherimportweatherconfig={'frequency':1,'start_date':'01-Jan-2020','end_date':'31-Jan-2020','api_key':'your api key here','location_list':['singapore'],'location_label':False}weather(config).get_weather_data()model moduleEvery ML model has a unique config. Please fill in accordingly.random forest# examples, please fill in according to your project scopefromnextstep.model.random_forestimportrandom_forestconfig={'label_column':'USEP','train_size':0.9,'seed':66,'n_estimators':10,'bootstrap':True,'criterion':'mse','max_features':'sqrt'}random_forest_shell=random_forest(config)random_forest_shell.build_model(data)# build modelarimafromnextstep.model.arimaimportarimaconfig={'lag':7,'differencing':0,'window_size':2,'label_column':'USEP','train_size':0.8,'seed':33}arima_shell=arima(config)arima_shell.autocorrelation(data)# plot autocorrelation to determine p, lag orderarima_shell.partial_autocorrelation(data)# plot partial autocorrelation to determine q, moving average widow sizearima_shell.build_model(data)# build model# residual plot to check model performancearima_shell.residual_plot()arima_shell.residual_density_plot()ContributingPull requests are welcomeAuthoryuesong YANGbolin ZHUZiyue Yang
Package: nextstep-plist
nextstep-plist is a python 3 compatible fork of the nsplist package
that provides a parser for the NeXTSTEP plist text-based format.
A common usage is parsing iTunes In App Purchase receipts to be
able to reject them early, without having to make a
request to the iTunes API.This is distinct from the Apple plist format, which has more
supported types and serialises to XML or binary.Free software: MIT licenseInstallationpip install nextstep-plistDocumentationhttps://python-nextstep-plist.readthedocs.io/DevelopmentTo run the all tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.1.0 (2017-06-25)First release on PyPI.
Package: nextstrain-augur
About NextstrainNextstrain is an open-source project to harness the scientific and public health potential of pathogen genome data.
We provide a continually-updated view of publicly available data with powerful analytics and visualizations showing pathogen evolution and epidemic spread.
Our goal is to aid epidemiological understanding and improve outbreak response.Resulting data and inferences are available live at the websitenextstrain.org.About AugurDefinition: One held to foretell events by omens.Augur is the bioinformatics toolkit we use to track evolution from sequence and serological data.
It provides a collection of commands which are designed to be composable into larger processing pipelines.The output of augur is a series of JSONs that can be used to visualize your results usingAuspice.QuickstartFollow instructions to install Augur.
Try out an analysis of real virus data bycompleting the Zika tutorial.DocumentationOverview of how Augur fits together with other Nextstrain toolsOverview of Augur usageTechnical documentation for AugurContributor guideDeveloper docs for AugurChangelogCitationHuddleston J, Hadfield J, Sibley TR, Lee J, Fay K, Ilcisin M, Harkins E, Bedford T, Neher RA, Hodcroft EB, (2021). Augur: a bioinformatics toolkit for phylogenetic analyses of human pathogens. Journal of Open Source Software, 6(57), 2906,https://doi.org/10.21105/joss.02906License and copyrightCopyright 2014-2022 Trevor Bedford and Richard Neher.Source code to Nextstrain is made available under the terms of theGNU Affero General Public License(AGPL). Nextstrain is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU Affero General Public License for more details.
Package: nextstrain-cli
Nextstrain CLIThis is the source code repository for a program callednextstrain, the
Nextstrain command-line interface (CLI).  It aims to provide a consistent way
to run and visualize pathogen builds and access Nextstrain components likeAugurandAuspiceacross computing platforms such asDocker,Conda,Singularity, andAWS Batch.Get started using the Nextstrain CLI by reading thedocumentation, which
includes installation and usage information.If you'd like to contribute to development, thedevelopment docsshould
help you get going.  We're glad you'd like to contribute!If you need help (or just want to say hi!),open an issueor send us an
email tohello@nextstrain.org.
Package: nextstrain-sphinx-theme
ASphinx themefor Nextstrain’s documentation, based onRead The Docs’
default theme (sphinx_rtd_theme).InstallationThis theme is distributed on PyPI asnextstrain-sphinx-themeand can be
installed withpip:$python3-mpipinstallnextstrain-sphinx-themeTo use the theme in your Sphinx project, you will need to add the following to
yourconf.pyfile:html_theme="nextstrain-sphinx-theme"This theme is based onsphinx_rtd_themeand accepts most of the sameconfiguration optionssettable viahtml_theme_optionand a few
additional options as well:logo:Boolean determining if the Nextstrain logo should be displayed.
Defaults to true.logo_link:URL to use for the logo’s link.  Defaults to
<https://docs.nextstrain.org>.logo_only:Ignored.  Inherited fromsphinx_rtd_theme.  Instead, the project
name and version info will not be displayed (thus showing only the
logo) whensubprojectis true.subproject:Boolean determining if this is a subproject of the main Nextstrain
documentation project on <https://docs.nextstrain.org>.  Defaults
to true.If your project wants to display its own logo, just set Sphinx’shtml_logoto point to the image file in your Sphinx project.html_logo="_static/your-logo.png"This will automatically take precedence over the default Nextstrain logo
provided by the theme.Developmentpython3-mpipinstall-e.makeclean# not always needed, but better to be cautiousmakehtmlopenbuild/html/index.htmlReleasingMake sure all your changes have been commited to themainbranch.Add a commit which describes the changes from the previous version toCHANGES.rstand updates the version number inlib/nextstrain/sphinx/theme/VERSION.Tag this commit with the version number, e.g.git tag-a2020.4-m"version 2020.4".Push the commit and tag to GitHub, e.g.git push origin main 2020.4.Publish to PyPI by invoking a GitHub Actions workflow:Go to the workflow:publish.yml.SelectRun workflow. In the new menu:SelectUse workflow from>Tags> new version number (e.g. 2020.4).SetPyPI instance for publishingasPyPI(default) orTestPyPI.More infoSelectRun workflow.TestingPR test builds are available here:https://readthedocs.org/projects/nextstrain-sphinx-theme/builds/and usually follow the following URL path convention:https://nextstrain–17.org.readthedocs.build/projects/sphinx-theme/en/17/where 17 is the PR number
Package: nextversion
A Python package to increments module verision numbers.fromnextversionimportnextversionnextversion('1.0a2')# => '1.0a3'nextversion('v1.0a2')# => '1.0a3'  (normalized to compatible version with PEP 386)nextversion('foo.0.3')# => None     (impossible to normalize)If original version number does not matchPEP 386,Next version compatible withPEP 386is returned if possible,If impossible,Noneis returned.InstallInstall from PyPI$pipinstallnextversionInstall from Github repo$gitclonehttps://github.com/laysakura/nextversion.git$cdnextversion$./setup.pyinstallSee alsoPEP 386Version::NextCPAN moduleAuthorSho Nakatani <lay.sakura@gmail.com>, a.k.a. @laysakura
Package: nextwave
Nextwave is a fork of the packageWavelink.
Nextwave is a robust and powerful Lavalink wrapper forNextcord.py.
Nextwave features a fully asynchronous API that’s intuitive and easy to use with built in Spotify Support and Node Pool Balancing.DocumentationNextwave Official Documentation.InstallationThe following commands are currently the valid ways of installing WaveLink.Nextwave requires Python 3.8+Windowspy-3.9-mpipinstall-UnextwaveLinuxpython3.9-mpipinstall-UnextwaveGetting StartedA quick and easy bot example:importnextwavefromnextcord.extimportcommandsclassBot(commands.Bot):def__init__(self):super().__init__(command_prefix='>?')asyncdefon_ready(self):print('Bot is ready!')classMusic(commands.Cog):"""Music cog to hold Wavelink related commands and listeners."""def__init__(self,bot:commands.Bot):self.bot=botbot.loop.create_task(self.connect_nodes())asyncdefconnect_nodes(self):"""Connect to our Lavalink nodes."""awaitself.bot.wait_until_ready()awaitnextwave.NodePool.create_node(bot=bot,host='0.0.0.0',port=2333,password='YOUR_LAVALINK_PASSWORD')@commands.Cog.listener()asyncdefon_nextwave_node_ready(self,node:nextwave.Node):"""Event fired when a node has finished connecting."""print(f'Node: <{node.identifier}> is ready!')@commands.command()asyncdefplay(self,ctx:commands.Context,*,search:nextwave.YouTubeTrack):"""Play a song with the given search query.

        If not connected, connect to our voice channel.
        """ifnotctx.voice_client:vc:nextwave.Player=awaitctx.author.voice.channel.connect(cls=wavelink.Player)else:vc:nextwave.Player=ctx.voice_clientawaitvc.play(search)bot=Bot()bot.add_cog(Music(bot))bot.run('YOUR_BOT_TOKEN')Lavalink InstallationHead to the officialLavalink repoand give it a star!Create a folder for storing Lavalink.jar and related files/folders.Copy and paste the exampleapplication.ymltoapplication.ymlin the folder we created earlier. You can open the yml in Notepad or any simple text editor.Change your password in theapplication.ymland store it in a config for your bot.Set local to true in theapplication.ymlif you wish to usenextwave.LocalTrackfor local machine search options… Otherwise ignore.Save and exit.InstallJava 17(Windows)orJava 13+on the machine you are running.DownloadLavalink.jarand place it in the folder created earlier.Open a cmd prompt or terminal and change directorycdinto the folder we made earlier.Run:java-jarLavalink.jarIf you are having any problems installing Lavalink, please join the official Discord Server listed above for help.
Package: next-word-prediction
Next Word PredictionGenerative Pretrained Transformer 2 (GPT-2) for Language Modeling using the PyTorch-Transformers library.InstallationRequires python>=3.5, pytorch>=1.6.0, pytorch-transformers>=1.2.0pipinstallnext-word-predictionHow to use>>>fromnext_word_predictionimportGPT2>>>gpt2=GPT2()>>>text="The course starts next">>>gpt2.predict_next(text,5)Thecoursestartsnext['week','to','month','year','Monday']Demo via Streamlitstreamlitrunapp/run.py
Package: nexus
Nexus is a pluggable admin application in Django. It’s designed to give you a simple design and architecture for building admin applications.(This project is still under active development)ScreenshotInstallInstall it with pip (or easy_install):pip install nexusConfigYou’ll need to enable it much like you woulddjango.contrib.admin.First, add it to yourINSTALLED_APPSsetting:INSTALLED_APPS = (
    ...
    'nexus',
)Now you’ll want to include it within yoururls.py:import nexus

# sets up the default nexus site by detecting all nexus_modules.py files
nexus.autodiscover()

# urls.py
urlpatterns = patterns('',
    ('^nexus/', include(nexus.site.urls)),
)By default Nexus requires django.contrib.auth and django.contrib.sessions. If you are using a custom auth system you can skip these requirements by using the settingNEXUS_SKIP_INSTALLED_APPS_REQUIREMENTS = Truein your django settings.ModulesNexus by default includes a module that will automatically pick updjango.contrib.admin.Other applications which provide Nexus modules:GargoyleMemcacheRedisdjango-debug-loggingDjango-Experiments(docs on writing modules coming soon)
Package: nexus-artifact-manager
Setuppip install nexus-artifact-managerUsage - Unix Type systempython -m nam $FORMAT $NEXUS_USER $NEXUS_PASSWORD $NEXUS_PROD_URL $ARTIFACT_PATH $ARTIFACT_NAME $REPOSITORY_NAME $NEXUS_DIRECTORY  -- raw formatpython -m nam $FORMAT $NEXUS_USER $NEXUS_PASSWORD $NEXUS_PROD_URL $ARTIFACT_PATH $ARTIFACT_NAME $REPOSITORY_NAME $NEXUS_GROUP_ID  -- maven2 formatUsage - Windows systempython -m nam %FORMAT% %NEXUS_USER% %NEXUS_PASSWORD% %NEXUS_PROD_URL% %ARTIFACT_PATH% %ARTIFACT_NAME% %REPOSITORY_NAME% %NEXUS_DIRECTORY%  -- raw formatpython -m nam %FORMAT% %NEXUS_USER% %NEXUS_PASSWORD% %NEXUS_PROD_URL% %ARTIFACT_PATH% %ARTIFACT_NAME% %REPOSITORY_NAME% %NEXUS_GROUP_ID%  -- maven2 format
Package: nexusconverter
Nexus Converter将 PyTorch 模型转换为 Nexus 代码。需要 PyTorch 模型的构造过程符合以下范式：classMyModel(torch.nn.Module):def__init__(self,num_class):super().__init__()""" 1. 定义每个网络层的参数 """self.conv1=Conv2d(...)self.relu1=ReLU()...defforward(self,x):""" 2. 前向计算 """x=self.conv1(x)x=self.relu1(x)...即，在__init__()中构造模型的树形结构（将子模块，也就是网络中的每一层，设为这个模型的属性），在forward()中描述前向计算。在前向计算中只使用__init__()中的子模块，而不使用torch.functional中的函数式的接口。然后，在主程序中首先引入convert模块中的toNexusCode：fromconvertimporttoNexusCode主程序中首先生成模型的实例，然后将其设为估值模式：model=MyModel()model.eval()toNexusCode只需要model和输入样例data即可导出代码：code=toNexusCode(model,data)生成的code为字符串的形式，如下：autodata0=Symbol::Variable("data0");autoconv1=Convolution("conv1",data0,{3,3},64,false,{1,1},{1,1},{1,1});autorelu1=Relu("relu1",conv1);autoconv2=Convolution("conv2",relu1,{3,3},64,false,{1,1},{1,1},{1,1});autorelu2=Relu("relu2",conv2);autopool1=Pooling("pool1",relu2,2,2,0);autoconv3=Convolution("conv3",pool1,{3,3},128,false,{1,1},{1,1},{1,1});autorelu3=Relu("relu3",conv3);autoconv4=Convolution("conv4",relu3,{3,3},128,false,{1,1},{1,1},{1,1});autorelu4=Relu("relu4",conv4);autopool2=Pooling("pool2",relu4,2,2,0);autoconv5=Convolution("conv5",pool2,{3,3},256,false,{1,1},{1,1},{1,1});autorelu5=Relu("relu5",conv5);autoconv6=Convolution("conv6",relu5,{3,3},256,false,{1,1},{1,1},{1,1});autorelu6=Relu("relu6",conv6);autoconv7=Convolution("conv7",relu6,{3,3},256,false,{1,1},{1,1},{1,1});autorelu7=Relu("relu7",conv7);autopool3=Pooling("pool3",relu7,2,2,0);autoconv8=Convolution("conv8",pool3,{3,3},512,false,{1,1},{1,1},{1,1});autorelu8=Relu("relu8",conv8);autoconv9=Convolution("conv9",relu8,{3,3},512,false,{1,1},{1,1},{1,1});autorelu9=Relu("relu9",conv9);autoconv10=Convolution("conv10",relu9,{3,3},512,false,{1,1},{1,1},{1,1});autorelu10=Relu("relu10",conv10);autopool4=Pooling("pool4",relu10,2,2,0);autoconv11=Convolution("conv11",pool4,{3,3},512,false,{1,1},{1,1},{1,1});autorelu11=Relu("relu11",conv11);autoconv12=Convolution("conv12",relu11,{3,3},512,false,{1,1},{1,1},{1,1});autorelu12=Relu("relu12",conv12);autoconv13=Convolution("conv13",relu12,{3,3},512,false,{1,1},{1,1},{1,1});autorelu13=Relu("relu13",conv13);autopool5=Pooling("pool5",relu13,2,2,0);autoflatten=Flatten("flatten",pool5);autofc1=FullyConnected("fc1",flatten,4096,false,true);autodrop1=Dropout("drop1",fc1,0.5);autofc2=FullyConnected("fc2",drop1,4096,false,true);autodrop2=Dropout("drop2",fc2,0.5);autofc3=FullyConnected("fc3",drop2,10,false,true);可以打印出来并粘贴到文本编辑器中。目前在 PyTorch 中可以被转换的算子包括：Conv2dMaxPool2dLinearDropoutReLUFlattenSigmoidTanhSoftmax
Package: nexus-databoard
nexus-databoardThis application provides the boilerplate to rapidly build data applications on the streamlit framework (which is already rapid!).Quick StartThere are a few options to get started rapidly.CloneClone the repository, and dopip install -r requirements.txtYou can add your own files to the./pagesdirectory to add/replace the example pages.Then do./runorstreamlit run ./src/nexus_databoard/Home.pyDocker ComposeRun the docker compose file to spin up the examples.You can also mount your own files in thepagesdirectory in the app.For easy development, copy the requirements into your workspace and get autocompletion for the dependencies.pip install -r requirements.txtExample Compose file that mounts a localpagesdirectoryservices:
  nexus_analytics:
    image: savantly/nexus-databoard:latest
    container_name: my-data-tools
    ports:
      - 8501:8501
    environment: []
    volumes:
      - ./pages:/app/pagesExtend the imageExtend the image and add your page files to thepagesdirectory.Example -FROM savantly/nexus-databoard
COPY ./pages /app/pages/
Package: nexusforge
Blue Brain Nexus Forge is a domain-agnostic, generic and extensible Python framework enabling
non-expert users to create and manage knowledge graphs by making it easy to:Discover and reuse available knowledge resources such as ontologies and
schemas to shape, constraint, link and add semantics to datasets.Build knowledge graphs from datasets generated from heterogenous sources and formats.
Defining, executing and sharing data mappers to transform data from a source format to a
target one conformant to schemas and ontologies.Interface with various stores offering knowledge graph storage, management and
scaling capabilities, for example Nexus Core store or in-memory store.Validate and register data and metadata.Search and download data and metadata from a knowledge graph.Getting StartedTheexamplesdirectory contains many Jupyter Notebooks to get started with
Blue Nexus Forge user features and usage scenarios.You can run the Getting Started notebooks on Binder by clicking on.For local execution, make sure that thejupyter notebook|labis launched
in the same virtual environment where Blue Brain Nexus Forge is installed.
Alternatively, set up a specializedkernel.In both cases, please start with the notebook named00 - Initialization.
It contains instructions for configuring the Forge with:an example in-memory store and an example schema language,Blue Brain Nexus as store and W3C SHACL as schema language.After, it is recommended to run the notebooks following their number (01, 02, …).InstallationIt is recommended to use a virtual environment such asvenvorconda environment.Stable versionpipinstallnexusforgeUpgrade to the latest versionpipinstall--upgradenexusforgeDevelopment versionpipinstallgit+https://github.com/BlueBrain/nexus-forgeFunding and AcknowledgementsThe development of this software was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology, and from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2).COPYRIGHT © 2019–2022 Blue Brain Project/EPFL