Saravanan666/pypi-org huggingface dataset:
Package: nidsdata
python-nidsdata这是一个封装了KDDCup99、NSL-KDD、UNSW-NB15等入侵监测数据集的Python包。安装pipinstallnidsdata使用KDDCup99fromnidsdataimportload_kddcup99_data"""load_kddcup99_data(index: int)index=0: 'kddcup.names',index=1: 'kddcup.data.gz',index=2: 'kddcup.data_10_percent.gz',index=3: 'kddcup.newtestdata_10_percent_unlabeled.gz',index=4: 'kddcup.testdata.unlabeled.gz',index=5: 'kddcup.testdata.unlabeled_10_percent.gz',"""# 获取namesdata=load_kddcup99_data(index=0)val_label=data.loc[0].apply(lambdaitem:item.split('.')[0])print(val_label,type(val_label))val_names=data.loc[1:,0].apply(lambdaitem:item.split(':')[0])print(val_names,type(val_names))NSL-KDDfromnidsdataimportload_naslkdd_data"""load_naslkdd_data(index: int)index=0: 'KDDTest+.txt',index=1: 'KDDTest-21.txt',index=2: 'KDDTrain+.txt',index=3: 'KDDTrain+_20Percent.txt'"""print(load_naslkdd_data(3))UNSW-NB15fromnidsdataimportload_unswnb15_data"""load_unswnb15_data(index: int)index=0: 'NUSW-NB15_features.csv',index=1: 'UNSW-NB15_1.csv',index=2: 'UNSW-NB15_2.csv',index=3: 'UNSW-NB15_3.csv',index=4: 'UNSW-NB15_4.csv',index=5: 'NUSW-NB15_GT.csv',index=6: 'UNSW-NB15_LIST_EVENTS.csv',index=7: 'UNSW_NB15_training-set.csv',index=8: 'UNSW_NB15_testing-set.csv'"""print(load_unswnb15_data(0))
Package: nifcloud
NIFCLOUD SDK for PythonTheNIFCLOUD SDK for Pythonis data-driven SDK.
It works by feeding AWS-SDK-compatible model JSONs to botocore module.Features:heavy_check_mark: Full support for NIFCLOUD Computing / RDB / NAS / Script / Hatoba / ESS / DNS / ObjectStorageService / ServiceActivity APIs:heavy_check_mark: The nifcloud package is the foundation for theNIFCLOUD CLI.:heavy_check_mark: AWS-SDK-compatible data-driven architectureRequirementsPython 3.7 or laterHow to Installpip install nifcloudUsageWrite your python program:fromnifcloudimportsessionclient=session.get_session().create_client("computing",region_name="jp-east-1",nifcloud_access_key_id="<Your NIFCLOUD Access Key ID>",nifcloud_secret_access_key="<Your NIFCLOUD Secret Access Key>")print(client.describe_instances())Execute the program:$ python test.pyCredentials and region name can be also passed via environment variables.fromnifcloudimportsessionclient=session.get_session().create_client("computing")print(client.describe_instances())$ export NIFCLOUD_ACCESS_KEY_ID=<Your NIFCLOUD Access Key ID>
$ export NIFCLOUD_SECRET_ACCESS_KEY=<Your NIFCLOUD Secret Access Key>
$ export NIFCLOUD_DEFAULT_REGION=jp-east-1
$ python test.pySeedocumentationfor detail.LicenseSeeLICENSE.txt.
Package: nifcloud-cli
nifcloud-cliUniversal Command Line Interface for NIFCLOUD ServicesRequirementsPython 3.7 or laterHow to InstallUsing pip$ pip install nifcloud-cli
$ nifcloud --versionUsing MSI installer (Windows only)Download the MSI installer fromRelease.Run the downloaded MSI installer.Check the nifcloud-cli from command prompt.C:\> nifcloud --versionUsing docker$ docker run -it --rm ghcr.io/nifcloud/nifcloud-cli --versionUsageSet credentialsYou can configure credentials via environment variables ornifcloud configurecommand.via environment variableLinux$ export NIFCLOUD_ACCESS_KEY_ID=<Your NIFCLOUD Access Key ID>
$ export NIFCLOUD_SECRET_ACCESS_KEY=<Your NIFCLOUD Secret Access Key>
$ export NIFCLOUD_DEFAULT_REGION=jp-east-1WindowsC:\> set NIFCLOUD_ACCESS_KEY_ID=<Your NIFCLOUD Access Key ID>
C:\> set NIFCLOUD_SECRET_ACCESS_KEY=<Your NIFCLOUD Secret Access Key>
C:\> set NIFCLOUD_DEFAULT_REGION=jp-east-1vianifcloud configurecommand$ nifcloud configure
NIFCLOUD Access Key ID [None]: <Your NIFCLOUD Access Key ID>
NIFCLOUD Secret Access Key [None]: <Your NIFCLOUD Secret Access Key>
Default region name [jp-east-1]: jp-east-1
Default output format [None]:config file is saved under the.nifclouddirectory in your home directory ($HOMEor%UserProfile%)Show available services$ nifcloud helpShow available actions for the service$ nifcloud computing helpShow available parameters for the action$ nifcloud computing create-key-pair helpRun the command actually$ nifcloud computing create-key-pair --key-name foobar123 --password foobar123LicenseSeeLICENSE.
Package: nifconverter
Build StatusCoverage
StatusPyPI versionUtility to translate NIF files across identifier schemes, for instance
between DBpedia and Wikidata.It can be used both as a Python library or as a CLI utility.Install it withpip install nifconverter.Example invocation:nifconverter-imy_dbpedia_nif_file.ttl-omy_wikidata_nif_file.ttlBy default we use theDBpedia SameThing
serviceto
convert URIs. It is also possible to query the DBpedia SPARQL endpoint
instead using the--converterparameter:nifconverter--converterFromDBpediaSparqlConverter-imy_dbpedia_nif_file.ttl-omy_wikidata_nif_file.ttlThe target URI space to convert to can be changed with the--targetoption:nifconverter--targethttps://en.wikipedia.org/wiki/-imy_wikidata_nif_file.ttl-omy_wikipedia_nif_file.ttl
Package: nif-dumper
NIF DumperA simple Python CLI tool to search and download NIF (Naruto Italian Forum) releasesTable of Contents📖 About The Project⚙️ Getting StartedPrerequisitesInstallationUsage💫 Contributing🤝 Support⚠️ License🛠️ Built With:book: About The ProjectA simple Python CLI tool to search and download NIF (Naruto Italian Forum) releases.This utility aims to make easier the download of a whole series due to missing pack downloadsIf you like NIF releases, support the team!This project is in no way affiliated with, supported or offered by NIF Team(back to top):gear: Getting StartedPrerequisitesMake sure to have Python 3.7+ and aria2 installed on your system.(back to top)InstallationYou can install this tool as a Python Module usingpipor executing the script directly.If you want to install the tool as a Python Module:Install the module using pip:pip install nif-dumperYou can run the tool using:nif-dumperIf you want to use the script directly:Clone the repository anywhere on your pc:git clone https://github.com/LightDestory/NIF-DumperInstall the requirements usingpip(create avenvif you want):pip install .Run the dumper:python ./src/nif_dumper/dumper.py(back to top)UsageThe dumper provides a wizard that will help you to download the releases.(back to top):dizzy: ContributingIf you are interested in contributing, please refer toContributing Guidelinesfor more information and take a look at open issues. Ask any questions you may have and you will be provided guidance on how to get started.Thank you for considering contributing.(back to top):handshake: SupportIf you find value in my work, please consider making a donation to help me create, and improve my projects.Your donation will go a long way in helping me continue to create free software that can benefit people around the world.(back to top):warning: LicenseThe content of this repository is distributed under the GNU GPL-3.0 License. SeeLICENSEfor more information.(back to top):hammer_and_wrench: Built WithPythonbeautifulsoup4InquirerPy(back to top)
Package: nifgen
Overall Statusmaster branch statusGitHub statusInfoPython bindings for NI Modular Instrument drivers. SeeGitHubfor the latest source.AuthorNIAboutThenifgenmodule provides a Python API for NI-FGEN. The code is maintained in the Open Source repository fornimi-python.Support Policynifgensupports all the Operating Systems supported by NI-FGEN.It followsPython Software Foundationsupport policy for different versions of CPython.NI-FGEN Python API StatusNI-FGEN (nifgen)Driver Version Tested Against2023 Q1.1PyPI VersionSupported Python VersionDocumentationOpen IssuesOpen Pull RequestsInstallationAs a prerequisite to using thenifgenmodule, you must install the NI-FGEN runtime on your system. Visitni.com/downloadsto download the driver runtime for your devices.The nimi-python modules (i.e. forNI-FGEN) can be installed withpip:$ python -m pip install nifgen~=1.4.7ContributingWe welcome contributions! You can clone the project repository, build it, and install it byfollowing these instructions.UsageThe following is a basic example of using thenifgenmodule to open a session to a Function Generator and generate a sine wave for 5 seconds.importnifgenimporttimewithnifgen.Session("Dev1")assession:session.output_mode=nifgen.OutputMode.FUNCsession.configure_standard_waveform(waveform=nifgen.Waveform.SINE,amplitude=1.0,frequency=10000000,dc_offset=0.0,start_phase=0.0)withsession.initiate():time.sleep(5)Other usage examples can be found on GitHub.Support / FeedbackFor support specific to the Python API, follow the processs inBugs / Feature Requests.
For support with hardware, the driver runtime or any other questions not specific to the Python API, please visitNI Community Forums.Bugs / Feature RequestsTo report a bug or submit a feature request specific to Python API, please use theGitHub issues page.Fill in the issue template as completely as possible and we will respond as soon
as we can.DocumentationDocumentation is availablehere.Licensenimi-pythonis licensed under an MIT-style license (see
LICENSE).
Other incorporated projects may be licensed under different licenses. All
licenses allow for non-commercial and commercial use.gRPC FeaturesFor driver APIs that support it, passing a GrpcSessionOptions instance as a parameter to Session.__init__() is
subject to the NI General Purpose EULA (see NILICENSE).
Package: nifi-api
NIFI REST APIAutomates the Nifi dataflowsDataflows can be fully automated using the Nifi API. This project taps and un-taps a dataflow by tracking the incoming/outgoing flowfiles.More specifically, it turns on aninitial processorand records the flowfiles generated by inspecting anincoming connection. After this, amiddle processoris turned on. When all the flowfiles are registered in aoutcoming connection, afinal processoris turned on. Before finishing, it returns the pipeline to its initial state.What is automated?The following example represents  the actions on the dataflow that get automated by thenifi_apilibrary.1. Consider the initial state of the dataflow:2. Turn on the "Initial" processor and turn off the "Final" processor:3. Turn off the "Initial" processor and record the flowfiles in the "Initial" connection, then turn on the "Middle" processor:4. Record the flowfiles in the "Final" Connection, when they coincide with the "Initial" flowfile turn off the "Middle" processor and turn on the "Final" processor.5. The flowfiles in the "Final" connection get consumed by the "Final" processor and the initial state of the dataflow is recovered.Here is a recording of the Nifi UI when the tool is being executed on this dataflow:EnvironmentThe Nifi cluster used for testing is in theCloudera Public Cloudand needs basic authentication credentials for accessing. The following are the environment variables required to access the cluster:- CLOUDERA_USER=user
  - CLOUDERA_PASS=password
  - CLOUDERA_CLUSTER=https://<url_clustername>.cloudera.site/<clustername>
  - CLOUDERA_NIFI_REST=/cdp-proxy-api/nifi-app/nifi-api/Installnipi_apiis inpipand can be installed by running:pip install nifi-apiHow to useConsider the templateTest_API.jsonin the root folder, this is the template used inWhat automates?section.Write the data structure with the Nifi Ids (located inview configuration -> settings -> Id) of the processors and connections:fromnifi_api.environmentimportDataFlowIdsids={"in_connection":{"Id":"cc549c6e-0177-1000-ffff-ffffb5d2aba2","name":"First"},"out_connection":{"Id":"51ab3b24-084f-1309-0000-00001946f2c7","name":"Final"},"in_processor":{"Id":"36c62ad6-d606-3b04-9743-d77b6249608c","name":"First"},"middle_processor":{"Id":"cc54862f-0177-1000-ffff-ffffe7325a20","name":"Middle"},"out_processor":{"Id":"51ab3b1e-084f-1309-a135-aa0100d7186b","name":"Final"},}data_ids=DataFlowIds(ids)Instantiate and run:fromnifi_api.dataflowimportDataFlowdataflow=DataFlow(dataflow_ids=data_ids,delay_seconds_after_start=5,delay_seconds_between_checks=5,)dataflow.run()pipeline watching has started..
Pipeline watching has finished ...
Package: nifi-deploy
CLI tool for exporting and importing Nifi templates.What it doesExport Process Groups as Nifi template XMLImport Nifi template XML into Nifi as a templateWhat it’s forThis tool stems from the need for a stricter governance for Nifi flow development.nifi-deployis primarily aimed at a continuous integration pipeline for
Nifi templates/general-usage Nifi flows.This CLI tool wraps the greatNiPyAPI by Chaffelsonand enables developers to integrate version control of Nifi flows in their regular
workflow with VCS.
Package: nifigator
NifigatorNifigator is a pure Python package for working with NLP in RDF. It uses theNLP Interchange Format (NIF)and theLexicon Model for Ontologiesand is build on top ofRDFLib.Here is what is does:Convert data from text documents to NIF data in RDFCurrently supported formats: txt, PDF (text, page and paragraph offsets)Add linguistic annotations from NLP processorsCurrently supported processor:StanzaCreate NifVector graphs that work like language models, this allow you tocreate explainable word vectors without random results, and tocombine word vectors with lexical and linguistic annotationsRDFLib is used to serialize and deserialize NIF data.See thedocumentationbuilt from the code.InstallationTo install Nifigator, run this command in your terminal:$pipinstallnifigatorTo install the package from Github$pipinstall-egit+https://github.com/denederlandschebank/nifigator.git
Package: nifi-rest
NIFI REST APIAutomates the Nifi dataflowsDataflows can be fully automated using the Nifi API. This project taps and un-taps a dataflow by tracking the incoming/outgoing flowfiles.More specifically, it turns on aninitial processorand records the flowfiles generated by inspecting anincoming connection. After this, amiddle processoris turned on. When all the flowfiles are registered in aoutcoming connection, afinal processoris turned on. Before finishing, it returns the pipeline to its initial state.What is automated?The following example represents  the actions on the dataflow that get automated by thenifi_apilibrary.1. Consider the initial state of the dataflow:2. Turn on the "Initial" processor and turn off the "Final" processor:3. Turn off the "Initial" processor and record the flowfiles in the "Initial" connection, then turn on the "Middle" processor:4. Record the flowfiles in the "Final" Connection, when they coincide with the "Initial" flowfile turn off the "Middle" processor and turn on the "Final" processor.5. The flowfiles in the "Final" connection get consumed by the "Final" processor and the initial state of the dataflow is recovered.Here is a recording of the Nifi UI when the tool is being executed on this dataflow:EnvironmentThe Nifi cluster used for testing is in theCloudera Public Cloudand needs basic authentication credentials for accessing. The following are the environment variables required to access the cluster:- CLOUDERA_USER=user
  - CLOUDERA_PASS=password
  - CLOUDERA_CLUSTER=https://<url_clustername>.cloudera.site/<clustername>
  - CLOUDERA_NIFI_REST=/cdp-proxy-api/nifi-app/nifi-api/Installnipi_apiis inpipand can be installed by running:pip install nifi-apiHow to useConsider the templateTest_API.jsonin the root folder, this is the template used inWhat automates?section.Write the data structure with the Nifi Ids (located inview configuration -> settings -> Id) of the processors and connections:fromnifi_api.environmentimportDataFlowIdsids={"in_connection":{"Id":"cc549c6e-0177-1000-ffff-ffffb5d2aba2","name":"First"},"out_connection":{"Id":"51ab3b24-084f-1309-0000-00001946f2c7","name":"Final"},"in_processor":{"Id":"36c62ad6-d606-3b04-9743-d77b6249608c","name":"First"},"middle_processor":{"Id":"cc54862f-0177-1000-ffff-ffffe7325a20","name":"Middle"},"out_processor":{"Id":"51ab3b1e-084f-1309-a135-aa0100d7186b","name":"Final"},}data_ids=DataFlowIds(ids)Instantiate and run:fromnifi_api.dataflowimportDataFlowdataflow=DataFlow(dataflow_ids=data_ids,delay_seconds_after_start=5,delay_seconds_between_checks=5,)dataflow.run()pipeline watching has started..
Pipeline watching has finished ...
Package: niflexlogger-automation
InfoNI FlexLogger API for PythonAuthorNIAboutTheniflexlogger-automationpackage contains an API (Application Programming
Interface) and examples for using Python to automateFlexLogger.
The automation API supports modifying the configuration of existing FlexLogger projects and
controlling the execution of FlexLogger tests.
The package is implemented in Python. NI created and supports this package.Requirementsniflexlogger-automationhas the following requirements:FlexLogger 2024 Q1+CPython 3.6 - 3.10. If you do not have Python installed on your computer, go to python.org/downloads to download and install it.InstallationTo installniflexlogger-automation, use one of the following methods:pip:$ python -m pip install niflexlogger-automationeasy_installfromsetuptools:$ python -m easy_install niflexlogger-automationDownload the project source and run:$ python setup.py installUsing the FlexLogger Python APIRefer to thedocumentationfor detailed information on how to useniflexlogger-automation.Refer toGetting Started with CompactDAQ and FlexLogger, for more information on installing FlexLogger, using hardware, or downloading FlexLogger examples.Contribution to the FlexLogger Python APIIf you would like to contribute to this API, first validate your changes using the provided automated tests. The Python API package contains a number of automated tests which should be used to
validate API changes before submitting a pull request. If a pull request contains
new API functionality, new automated tests that exercise the new functionality
should be included with the pull request.To run the automated tests for the Python API, you must first configure FlexLogger
to load the test plugins that the test projects use. To do this, copytests/assets/pythonTests.configto%public%\Documents\NationalInstruments\FlexLogger\Plugins\IOPlugins, and in that
file replace<path to git repo>with the path to the cloned repo.After this is done, you can run the tests withtox.Support / FeedbackTheniflexlogger-automationpackage is supported by NI. For support forniflexlogger-automation, open a request through the NI support portal atni.com.Bugs / Feature requestsTo report a bug or submit a feature request, use theGitHub issues page.Licenseniflexlogger-automationis licensed under an MIT-style license (seeLICENSE).  Other incorporated projects may be licensed under different
licenses. All licenses allow for non-commercial and commercial use.
Package: niflow-manager
niflow-manager: Niflow package manager toolNiflows are an organizational structure that is targeted at making neuroimaging
tools and analyses FAIR (findable, accessible, interoperable, and reusable) ith
strong assurances of compatibility across environments.niflow-manager, which provides thenfmcommand-line tool, aims to support
niflow creation, testing, and packaging.
It provides the following sub-commands:nfm init- Create a stub workflow, with templates for desired languagesnfm build- Package a workflow into supported formats, includingSingularityand language-specific formatsnfm test- Comprehensive testing across ranges of environments and dependency
versionsnfm install- Install niflows from an online registry or sourceOther resourcesIntroduction to Niflows
Package: niflow-nipype1-workflows
Nipype 1 legacy workflowsThis package includes the workflows built up over nearly a decade of development ofNipype.These workflows were contributed by various users and developers, and are more-or-less tied to the
specific contexts, in terms of available tools and interfaces, as well as consensus best practices,
at the time each was developed. It is laborious to identify which have been updated to accommodate
changes in underlying software or changing ideas of best practice, and which serve primarily as a
public record of a particular effort.The Nipype developers have decided to initiate theniflowsproject,
which will allow new workflows, or collections of workflows, to be separately packaged and made
available under a common interface and in a common namespace. This will permit each niflow
repository and associated package to describe its history in a natural way, and the level of
maintenance of a package will not be obscured by the development process of nipype itself.This package serves as an example niflow. In general, it exists as a historical record, and updates
to this package will be for the sake of further developing niflows. If a bug is found in one of
these workflows that was not introduced by moving the workflows out of nipype, then the preferred
response is to publish a new niflow.
Package: nifpga
FPGA Interface Python APIOverviewThe National Instruments FPGA Interface Python API is used for communication between processor and FPGA within NI reconfigurable I/O (RIO) hardware such as NI CompactRIO, NI Single-Board RIO, NI FlexRIO, and NI R Series multifunction RIO.With the FPGA Interface Python API, developers can use LabVIEW FPGA to program the FPGA within NI hardware and communicate to it from Python running on a host computer. This gives engineers and scientists with Python expertise the ability to take advantage of compiled LabVIEW FPGA bitfiles, also the option to reuse existing Python code.InstallationNiFpga can be installed by cloning the master branch and then in a command
line in the directory of setup.py run:pip install --pre .Or by installing from PyPI using:pip install nifpganifpga supports Python 3.5+.ExamplesThe FPGA Interface Python API is session based. LabVIEW FPGA will generate
bitfiles (.lvbitx) that can be used to program the hardware. For additional
information on sessions view our Read the docs documentationExample usage of FPGA configuration functions:with Session(bitfile="BitfilePath.lvbitx", resource="RIO0") as session:
    session.reset()
    session.run()
    my_control = session.registers["MyControl"]
    my_control.write(4)
    data = my_control.read()See ourreadthedocs pagefor more detailed examples and documentation.
Package: nifstd-tools
nifstd-toolsTools for working with theNIF-Ontology.InstallationThe use of nifstd-tools is highly intertwined with thepyontutils respository.
If you have a use case (e.g.ontree) wherenifstd-toolsworks as a
stand-alone package install it usingpip install --user nifstd-tools.
Otherwise see below for the full development installation instructions.ConfigurationSee pyontutilsconfiguration.Development InstallationFollow theparent instructionsand refer to the rest of this section if you encounter issues.Note that the optional development packages are not strictly required. If you have
installation issues development can proceed normally without them, but some database
queries will be slower because they use a pure python mysql connector.If you don't want to use pipenv (which is probably most people) then the quickest
way to get up and running with a development install ispip install --user --editable .from the folder that contains this readme.WorkflowsSee the documentation forNIF-Ontology processes.
Package: nifti2png
My purpose nifti 2 png converterThis is a personal nifti 2 png converter. This is a helper to quickly view a
lot of nifti files by converting them to png.Installationpip install nifti2pngUsageIt takes two arguments:nifti2png --dir <input directory of nifti files (recursively)> --out <output directory where the pngs should be stored>
Package: niftianon
Anonymiser for NIFTI format images.InstallationLatest stable release from pippip install niftianonLatest development version from Githubgit clone https://github.com/jstutters/niftianon
cd niftianon
pip install .Usageniftianon [path to identifiable image] [path to anonymised image]The anonymised image path must not exist.ContributeIssue Tracker:github.com/jstutters/niftianon/issuesSource Code:github.com/jstutters/niftianonSupportIf you are having problems, please let me know by submitting an issue in thetracker.Licenseniftianon is licensed under theMIT license.