Saravanan666/pypi-org huggingface dataset:
Package: niswitch
Overall Statusmaster branch statusGitHub statusInfoPython bindings for NI Modular Instrument drivers. SeeGitHubfor the latest source.AuthorNIAboutTheniswitchmodule provides a Python API for NI-SWITCH. The code is maintained in the Open Source repository fornimi-python.Support Policyniswitchsupports all the Operating Systems supported by NI-SWITCH.It followsPython Software Foundationsupport policy for different versions of CPython.NI-SWITCH Python API StatusNI-SWITCH (niswitch)Driver Version Tested Against2023 Q1PyPI VersionSupported Python VersionDocumentationOpen IssuesOpen Pull RequestsInstallationAs a prerequisite to using theniswitchmodule, you must install the NI-SWITCH runtime on your system. Visitni.com/downloadsto download the driver runtime for your devices.The nimi-python modules (i.e. forNI-SWITCH) can be installed withpip:$ python -m pip install niswitch~=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 theniswitchmodule to open a session to a Switch and connect channels.importniswitchwithniswitch.Session("Dev1")assession:session.connect(channel1='r0',channel2='c0')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: nisyscfg
NI System Configuration Python APIPython bindings for NI System Configuration. SeeGitHubfor the latest source.Author: National InstrumentsAboutThenisyscfgpackage contains an API (Application Programming Interface)
for interacting with NI System Configuration. The package is implemented in Python.
This package was created by NI. The package is implemented as a complex, highly
object-oriented wrapper around the NI System Configuration C API using thectypesPython library.nisyscfgsupports only the Windows and Linux operating systems.nisyscfgsupports CPython 3.7+.InstallationNote: Runningnisyscfgrequires the NI System Configuration Runtime. Visit theni.com/downloadsto download the latest version
of NI System Configuration.Installnisyscfgfrom PyPI:$pipinstallnisyscfgOr, installnisyscfgby cloning the main branch and running the following command in the directory of setup.py:$pipinstall--pre.UsageThe following is a basic example of using annisyscfg.Sessionobject.>>>importnisyscfg>>>withnisyscfg.Session()assession:>>># Print user aliases for all National Instruments devices in the local system>>>filter=session.create_filter()>>>filter.is_present=True>>>filter.is_ni_product=True>>>filter.is_device=True>>>forresourceinsession.find_hardware(filter):>>>print(resource.expert_user_alias[0])
Package: nisystemlink-clients
InfoNI SystemLink API Clients for PythonAuthorNational InstrumentsAboutThenisystemlink-clientspackage contains an API (Application Programming
Interface) forSystemLinkthat uses HTTP to
interact with a SystemLink Server. The package is implemented in Python. NI
created and supports this package.Requirementsnisystemlink-clientshas the following requirements:A SystemLink Server installation or aSystemLink Cloudaccount to connect toCPython 3.8+InstallationTo installnisystemlink-clients, use one of the following methods:pip:$ python -m pip install nisystemlink-clientseasy_installfromsetuptools:$ python -m easy_install nisystemlink-clientsUsageRefer to thedocumentationfor detailed information on how to usenisystemlink-clients.Support / FeedbackThenisystemlink-clientspackage is supported by NI. For support fornisystemlink-clients, open a request through the NI support portal atni.com.Bugs / Feature RequestsTo report a bug or submit a feature request, please use theGitHub issues page.DocumentationTo view the documentation, visit thenisystemlink.clients Documentation Page.ChangelogTo view the changelog, visit theGitHub repository CHANGELOG.Licensenisystemlink-clientsis 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: nisystemlink-configuration
===========  ====================================================
Info         NI SystemLink Configuration Utility
Author       National Instruments
===========  ====================================================.. _about_section:AboutThenisystemlink-configurationpackage contains a command line utility for IT professionals to use
when managing the configuration of an NISystemLink Server <https://ni.com/systemlink>_ installation... image::https://badge.fury.io/py/ni/nisystemlink-configuration.svg:target:https://badge.fury.io/py/ni/nisystemlink-configuration.. _requirements_section:Requirementsnisystemlink-configurationhas the following requirements:A SystemLink Server installationVersion 2020R3 or laterSingle-box installationPythonVersion >= 3.7 -Python Installers <https://www.python.org/downloads/>_PATH environment variable.. _usage_section:UsageSee thedocs <docs>_ for documentation on usage of the utility... _contributing_section:ContributingSee thecontributing guideline <CONTRIBUTING.md>_ for how to set up and submit
contributions to thenisystemlink-configuration.
Package: nitasthecat
PyPi:https://pypi.org/project/nitasthecat/Hello there, This package shows my cat all details i hope you enjoy.how to installopen CMD / Terminalpipinstallnitasthecathow to use this packageopen IDLE then type…fromnitasthecatimportNitascat=Nitas()cat.show_name()cat.about()cat.show_ascii()cat.show_youtubecat.show_IG()developed by beamtan github:https://github.com/beamtan
Package: nitclk
Overall Statusmaster branch statusGitHub statusInfoPython bindings for NI Modular Instrument drivers. SeeGitHubfor the latest source.AuthorNIAboutThenitclkmodule provides a Python API for NI-TClk. The code is maintained in the Open Source repository fornimi-python.Support Policynitclksupports all the Operating Systems supported by NI-TClk.It followsPython Software Foundationsupport policy for different versions of CPython.NI-TClk Python API StatusNI-TClk (nitclk)Driver Version Tested Against2023 Q2PyPI VersionSupported Python VersionDocumentationOpen IssuesOpen Pull RequestsInstallationAs a prerequisite to using thenitclkmodule, you must install the NI-TClk runtime on your system. Visitni.com/downloadsto download the driver runtime for your devices.The nimi-python modules (i.e. forNI-TClk) can be installed withpip:$ python -m pip install nitclk~=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 thenitclkmoduleimportnitclkOther 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: nitdms
nitdms- A pythonic TDMS file readerThe main export from thenitdmspackage is the TdmsFile class. Upon instantiation,
the reader loads the file into memory and discovers all of the file, group and channel
objects and respective properties. These objects and properties are dynamically
instantiated as attributes allowing easy access from within an interactive session
with tab completion such as Jupyter or bash.Channel data is returned as a numpy ndarray, or WaveformDT if data in the file is from
LabVIEW's waveform data type. WaveformDT is a subclass of ndarray that mimics
the waveform data type.
LabVIEW's timestamp is stored as UTC in the TDMS file.nitdmsreturns the timestamp
in the machine's local time zone consistent with LabVIEW and aligns with WaveformDT.Installing$pipinstallnitdmsUsageWithin an interactive session with tab completion:>>>fromnitdmsimportTdmsFile>>>tf=TdmsFile(<file>)>>>data=tf.<group>.<channel>.data>>>t0=tf.<group>.<channel>.wf_start_time>>>dt=tf.<group>.<channel>.wf_increment>>>group_property=tf.<group>.<property>Documentationhttps://l-johnston.github.io/nitdms/
Package: nit-dpi-validator
# nit_dpi_validator
Validador de NIT, DPI GuatemalaThe project is hosted on GitHub. Contributions are welcome.GitHub: [monroy95/nit_dpi_validator: Validador de NIT, DPI Guatemala (github.com)](https://github.com/monroy95/nit_dpi_validator)InstalaciónLa forma fácil de instalarnit_dpi_validatores usandopip:pip install nit_dpi_validatorUsoValidando NIT>>> from nit_dpi_validator import nit
>>> nit.nit_validator('xxxxxxxx-x')
TrueValidando DPI>>> from nit_dpi_validator import dpi
>>> dpi.dpi_validator('xxxxxxxxxxxxx')
True
Package: nitec
NITEC: Versatile Hand-Annotated Eye Contact Dataset for Ego-Vision Interaction (Accepted at WACV24)CitingIf you find our work useful, please cite the paper:@misc{hempel2023nitec,title={NITEC: Versatile Hand-Annotated Eye Contact Dataset for Ego-Vision Interaction},author={Thorsten Hempel and Magnus Jung and Ahmed A. Abdelrahman and Ayoub Al-Hamadi},year={2023},eprint={2311.04505},archivePrefix={arXiv},primaryClass={cs.CV}}PaperThorsten Hempel, Magnus Jung, Ahmed A. Abdelrahman and Ayoub Al-Hamadi, "NITEC: Versatile Hand-Annotated Eye Contact Dataset for Ego-Vision Interaction",accepted at WACV 2024.AbstractEye contact is a crucial non-verbal interaction modality and plays an important role in our everyday social life. While humans are very sensitive to eye contact, the capabilities of machines to capture a person's gaze are still mediocre. We tackle this challenge and present NITEC, a hand-annotated eye contact dataset for ego-vision interaction. NITEC exceeds existing datasets for ego-vision eye contact in size and variety of demographics, social contexts, and lighting conditions, making it a valuable resource for advancing ego-vision-based eye contact research. Our extensive evaluations on NITEC demonstrate strong cross-dataset performance, emphasizing its effectiveness and adaptability in various scenarios, that allows seamless utilization to the fields of computer vision, human-computer interaction, and social robotics. We make our NITEC dataset publicly available to foster reproducibility and further exploration in the field of ego-vision interaction.Quick Usage:pipinstallface_detection@git+https://github.com/elliottzheng/face-detection
pipinstallnitecExample usage:fromnitecimportNITEC_Classifier,visualizeimportcv2nitec_pipeline=NITEC_Classifier(weights=CWD/'models'/'nitec_rs18_e20.pth',device=torch.device('cuda')# or 'cpu')cap=cv2.VideoCapture(0)_,frame=cap.read()# Process frame and visualizeresults=nitec_pipeline.predict(frame)frame=visualize(frame,results,confidence=0.5)Train / TestNITEC DatasetPrepare the dataset as explainedhere.SnapshotsDownload from here:https://drive.google.com/drive/folders/1zc6NZZ6yA4NJ52Nn0bgky1XpZs9Z0hSJ?usp=sharingTrainpythontrain.py\--gpu0\--num_epochs50\--batch_size64\--lr0.0001\Testpythontest.py\--snapshotmodels/nitec_rs18_20.pth\--gpu0\
Package: niteoweb.downloadcounter
Introductionniteoweb.downloadcounter is a simple Plone add-on. It counts how many times has a default
File content type been downloaded. It displays this value in a viewlet below File’s title.InstallingThis package requires Plone 4.0 or later (tested on 4.0a4).Installing without buildoutInstall this package in either your system path packages or in the lib/python
directory of your Zope instance. You can do this using either easy_install or
via the setup.py script.Installing with buildoutIf you are usingbuildoutto manage your instance installing
niteoweb.downloadcounter is even simpler. You can install
niteoweb.downloadcounter by adding it to the eggs line for your instance:[instance]
eggs = niteoweb.downloadcounterAfter updating the configuration you need to run the ‘’bin/buildout’’, which
will take care of updating your system.Go to the ‘Site Setup’ page in the Plone interface and click on the ‘Add/Remove Products’ link.Choose the product (check its checkbox) and click the ‘Install’ button.Changelog for niteoweb.downloadcounter1.0 (2010-02-06)Registers a viewlet that displays download count value on ATFile view page. [zupo]Overrides at_download Script (Python) so that it also increases the download count value. [zupo]Use archetypes.schemaextender to add an integer field ‘download count’ to ATFile. [zupo]Initial release. [zupo]
Package: niteoweb.transmogrifier.simpleusage
IntroductionThis package shows you how to use collective.transmogrifier to import news items into Plone.By default, it uses dummy values that are hard-coded in the product itself to create some News Items,
so you can see how to even use transmogrifier.The main idea is that you can use this product as a skeleton for migrating content from other systems to Plone,
since you only need to pass in correct data instead of hard-coded dummy values.Examples of real-life parsing HTML to extract data you need can be found in /branches folder of the product’s repository athttp://svn.plone.org/svn/collective/niteoweb.transmogrifier.simpleusage/branches/.The product sets the following fields on newly created News Items:titletextdatecategoryauthorDemoRun the following set of commands:$ cd <workspace>
$ virtualenv -p python2.4 --no-site-packages simpleusage
$ cd simpleusage
$ svn co https://svn.plone.org/svn/collective/niteoweb.transmogrifier.simpleusage/trunk ./
$ bin/python bootstrap.py
$ bin/buildout
$ bin/instance fgThen start your favorite browser and point it tohttp://localhost:8080/manageto add a new Plone site.
You can now use the following URL to start importing News Items:http://localhost:8080/<your_site_id>/@@importCustomizingFirst, create a branch for you to work on:$ cd <workspace>
$ virtualenv -p python2.4 --no-site-packages johns_migration
$ cd johns_migration
$ svn copy https://svn.plone.org/svn/collective/niteoweb.transmogrifier.simpleusage/trunk https://svn.plone.org/svn/collective/niteoweb.transmogrifier.simpleusage/branches/johns_migrationThen, checkout your branch and build a development environment:$ svn co https://svn.plone.org/svn/collective/niteoweb.transmogrifier.simpleusage/branches/johns_migration ./
$ bin/python bootstrap.py
$ bin/buildoutNow you have everything you need to start your own migration. Open <workspace>/johns_migration/src/niteoweb.transmogrifier.simpleusage/transmogrifier.py in your favorite text editor.
Scroll all the way to the bottom to find a method ‘source’ and modify it so that it parses data from your source and returns it as a list of dicts.Once you’re done, start Zope and point your browser tohttp://localhost:8080/<your_site_id>/@@import to start importing!Changelog1.0.1 (2010-07-26)Added newlines to long description.1.0 (2010-07-26)Initial release. [zupo]
Package: niteru
niteruThis package provides a set of functions to measure the similarity between HTMLs.Note: This is a fork ofhtml-similarity.Key differencesType hintsAll functions have proper type hintsDependency freeWorks along with plain PythonInstallationpipinstallniteruHow it worksStructural SimilarityUses sequence comparison of the html tags to compute the similarity.We do not implement the similarity based on tree edit distance because it is slower than sequence comparison.Style SimilarityExtracts CSS classes of each html document and calculates the jaccard similarity of the sets of classes.Joint Similarity (Structural Similarity and Style Similarity)The joint similarity metric is calculated as::k*structural_similarity(html1,html2)+(1-k)*style_similarity(html1,html2)All the similarity metrics take values between 0.0 and 1.0.Recommendations for joint similarityUsingk=0.3gives better results. The style similarity gives more information about the similarity rather than the structural similarity.ExamplesHere is an example:html1='''<h1 class="title">First Document</h1><ul class="menu"><li class="active">Documents</li><li>Extra</li></ul>'''html2='''<h1 class="title">Second document Document</h1><ul class="menu"><li class="active">Extra Documents</li></ul>'''fromniteruimportstyle_similarity,structural_similarity,similaritystyle_similarity(html1,html2)# => 1.0structural_similarity(html1,html2)# => 0.8571428571428571similarity(html1,html2)# => 0.9285714285714286
Package: nith-results
Nith_resultsNith_resultsis a terminal app to extract result of NITH.InstallationInstall using pip (Recommended)Use pip to install, user--userflagpython3 -m pip install --user nith_resultsBuild from SourceClone the repository and checkout to stable commitgit clone https://github.com/srbcheema1/nith_results
cd nith_results
git checkout <latest_version say: v0.0.x>install requirementspython3 -m pip install --user -r requirements.txtInstall nith_resultspython3 setup.py install --userBuilding Source Distributionpython3 setup.py sdistAvailable StatsResults are organised in folders for each branch inresult directoryBranch wise results are present in folders named with branch with files asbatch_xx_xgpi.txtorbatch_xx_xgpi.json.Full Year-wise and Full college wise results are also available in foldersFULL_YEARandFULL_COLLEGErespectively.Result is available in text and json format inresult/textandresult/jsondirectories respectivelyFolders contain results inbranch_name_xx_xgpi.txt.Also you can find outfull_year_xx_xgpi.txtcontaining data for whole year.Contact / Social MediaDevelopment byDeveloper / Author:Srb Cheema
Package: nitime
===================================================Nitime: timeseries analysis for neuroscience data===================================================Nitime contains a core of numerical algorithms for time-series analysis both inthe time and spectral domains, a set of container objects to representtime-series, and auxiliary objects that expose a high level interface to thenumerical machinery and make common analysis tasks easy to express with compactand semantically clear code.Website=======Current information can always be found at the NIPY website is locatedhere::http://nipy.org/nitimeMailing Lists=============Please see the developer's list here::https://mail.python.org/mailman/listinfo/neuroimagingCode====You can find our sources and single-click downloads:* `Main repository`_ on Github.* Documentation_ for all releases and current development tree.* Download as a tar/zip file the `current trunk`_.* Downloads of all `available releases`_... _main repository: http://github.com/nipy/nitime.. _Documentation: http://nipy.org/nitime.. _current trunk: http://github.com/nipy/nitime/archives/master.. _available releases: http://github.com/nipy/nitime/downloadsLicense information===================Nitime is licensed under the terms of the new BSD license. See the file"LICENSE" for information on the history of this software, terms & conditionsfor usage, and a DISCLAIMER OF ALL WARRANTIES.All trademarks referenced herein are property of their respective holders.Copyright (c) 2006-2023, NIPY DevelopersAll rights reserved.
Package: nitk
Network Inference ToolkitA bunch of scripts written to infer correlation/partial correlation networks from data. The goal is to have them like sklearn models. Currently very much
a work in progressImplemented:
SPACE - Partial Correlation Estimation by Joint Sparse Regression Models  by Peng, Wang and Zhu -https://doi.org/10.1198/jasa.2009.0126SCIO - Fast and adaptive sparse precision matrix estimation in high dimensions - Liu and Luo -https://doi.org/10.1016/j.jmva.2014.11.005CLIME - A Constrained L1 Minimization Approach to Sparse Precision Matrix Estimation - Cai, Liu and Luo -https://doi.org/10.1198/jasa.2011.tm10155DTrace - Sparse precision matrix estimation via lasso penalized D-trace loss - Zou and Zhang -https://doi.org/10.1093/biomet/ast059Correlation Permutation - Estimates a sparse correlation matrix by permuting the dataset repeatedly to get a p-value to see if
the correlation between two variables is just as likely to occur through noise
Scaled Lasso - "Sparse Matrix Inversion with Scaled Lasso" by Sun and Zhang -http://www.jmlr.org/papers/volume14/sun13a/sun13a.pdf
Package: nitor-vault
nitor-vaultCommand line tools and libraries for encrypting keys and values using client-side encryption with AWS KMS keys.InstallationThe easiest install is the python package from pypi:pip install nitor-vaultJavascript and java versions are available from npm and maven central respectively and installation will depend on your needs.Example usageInitialize vault bucket and other infrastructure:vault --init. Will create a CloudFormation stack.Encrypt a file and store in vault bucket:vault -s my-key -f <file>Decrypt a file:vault -l <file>Encrypt a single value and store in vault bucketvault -s my-key -v my-valueDecrypt a single valuevault -l my-keyUsing encrypted CloudFormation stack parametersEncrypt a value like this:$ vault -e 'My secret value'The command above will print the base64 encoded value encrypted with your vault KMS key. Use that value in a CF parameter. The value is then also safe to commit into version control and you can use it in scripts for example like this:#!/bin/bash

MY_ENCRYPTED_SECRET="AQICAHhu3HREZVp0YXWZLoAceH1Nr2ZTXoNZZKTriJY71pQOjAHKtG5uYCdJOKYy9dhMEX03AAAAbTBrBgkqhkiG9w0BBwagXjBcAgEAMFcGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMYy/tKGJFDQP6f9m1AgEQgCq1E1q8I+btMUdwRK8wYFNyE/5ntICNM96VPDnYbeTgcHzLoCx+HM1cGvc"


UNENCRYPTED_SECRET="$(vault -y $MY_ENCRYPTED_SECRET)"Obviously you need to make sure that in the context of running vault there is some sort of way for providing kms permissions by for example adding the decryptPolicy managed policy from the vault cloudformation stack to the ec2 instance or whatever runs the code.To decrypt the parameter value at stack creation or update time, use a custom resource:Parameters:
  MySecret:
    Type: String
    Description: Param value encrypted with KMS
Resources:
  DecryptSecret:
    Type: "Custom::VaultDecrypt"
    Properties:
      ServiceToken: "arn:aws:lambda:<region>:<account-id>:function:vault-decrypter"
      Ciphertext: { "Ref": "MySecret" }
  DatabaseWithSecretAsPassword:
    Type: "AWS::RDS::DBInstance"
    Properties:
      ...
      MasterUserPassword:
        Fn::Sub: ${DecryptSecret.Plaintext}LicenceApache 2.0