Saravanan666/pypi-org huggingface dataset:
Package: nicovideo-api-client
NicoApiClient概要ニコニコ動画 『スナップショット検索API v2』などの API について、仕様をなるべく意識せずに利用できるクライアントを提供する。installPyPIリポジトリ:https://pypi.org/project/nicovideo-api-client/pipinstallnicovideo-api-clientinstalledCode ClimatedocumentationNicoApiClient コードドキュメントexamplefromnicovideo_api_client.api.v2.snapshot_search_api_v2importSnapshotSearchAPIV2fromnicovideo_api_client.constantsimportFieldTypejson=SnapshotSearchAPIV2()\.tags_exact()\.single_query("VOCALOID")\.field({FieldType.TITLE,FieldType.CONTENT_ID})\.sort(FieldType.VIEW_COUNTER)\.no_filter()\.limit(100)\.user_agent("NicoApiClient","0.5.0")\.request()\.json()利用規約https://site.nicovideo.jp/search-api-docs/snapshot#api%E5%88%A9%E7%94%A8%E8%A6%8F%E7%B4%84
Package: nic-parser
nic_parserA python library to parse Sri Lankan national identity card numbers.FeaturesSupports both new and old NIC types.Installationgit clone https://github.com/chehanr/nic_parser.git && cd nic_parser
python setup.py installOrpip install nic-parserUsage>>> from nic_parser.parser import Parser
>>> nic = Parser('952903022V')
>>> nic.birth_date
datetime.datetime(1995, 10, 16, 0, 0)
>>> nic.gender
<Gender.MALE: 0>
>>> nic.serial_number
302
>>> nic.check_digit
2
>>> nic.special_letter
'V'
>>> nic.id_type
<IdType.OLD: 0>
>>>TODOAdd NIC number generation....
Package: nicrypt
Nicrypt basic encryption algorithmThis is a simple package used to encrypt and decrypt both brief and long texts. The texts can be some secrets, e.g.api keys and secrets, cryptocurrency wallet keys, wallet recovery phrases etc. All you have to remember is it uses the same keys in decryption for for what was used for encryption and therefore you have to record and keep them in a safer place.UsageIn scriptsIn your python script simply import the module and use as followsfrom nicrypt.encrypt import fichaencrypted_text = ficha('text to encrypt', 'enc'). This will encrypt the suplied text.You can reverse the process by decrypting the text by keying in the followingdecrypted_ext = ficha(f'{encrypted_text}', 'dec')# notice the usage of f-strings and thus python3.7+ is needed.NOTE: The above methods uses a specific set of encryption key and salt that you must provide. These are required to be saved as environment variables and stored as 'env_vars' dictionary like so:env_vars = {
	'key': os.environ.get('ENC_KEY'),
	'salt': os.environ.get('ENC_SALT')
}NB: ficha means 'hide' in Kiswahili
Package: nics
Now I Can SleepA minimalist, readable, easy-to-configure, and markdown-based automated documentation workflow for GitHub repositories.🐈BEFORE🐈

           ┌──────────>  Make changes  ────────────┐
           |                                       v

    Deploy docs-page                       Update docs-files

           ^                                       │
           └─────────  Rebuild docs-page  <────────┘



🐾with NICS🐾

           ┌──────────>  Make changes  ────────────┐
           |                                       |
           └─────────  Update docs-files  <────────┘InstallationInstall via pip, runpip install nicsNavigate to your project root folderRunnics initFollow the wizard, and you all set!LinksDocumentationChangelog💙Many ThanksFAQTroubleshootTo report bugs or ask questions, please open an issue or submit a pull request.LicenseThis project is licensed under the MIT license.
Package: nida
NidaUnofficial package for fetching users information based on National ID Number made bykalebuInstallationYou can install it directly or using pipHere how to install directlygitclonehttps://github.com/Kalebu/NidacdNida
Nida->pythonsetup.yinstallHere how to install from pippipinstallnidaUsageTo fetch user information based on ID number do this;>>>fromnidaimportload_user>>>user_detail=load_user(national_id='XXXXXXXXX')>>>print(user_detail)user{'Nin':'XXXXXX','Firstname':'XXXXXX','Middlename':'XXXXXX','Surname':'XXXXXX','Othernames':'XXXXXX','Sex':'XXXXXX','Dateofbirth':'XXXXXX','Residentregion':'XXXXXX','Residentdistrict':'XXXXXX','Residentward':'XXXXXX','Residentvillage':'XXXXXX','Residentstreet':'XXXXXX','Residentpostcode':'XXXXXX','Permanentregion':'XXXXXX','Permanentdistrict':'XXXXXX','Permanentward':'XXXXXX','Permanentvillage':'XXXXXX','Permanentstreet':'XXXXXX','Birthcountry':'XXXXXX','Birthregion':'XXXXXX','Birthdistrict':'XXXXXX','Birthward':'XXXXXX','Nationality':'XXXXXX','Phonenumber':'XXXXXX','Maritalstatus':'XXXXXX','Occupation':'XXXXXX','Primaryschooleducation':'XXXXXX','Primaryschooldistrict':'XXXXXX','Primaryschoolyear':'XXXXXX','Photo':'XXXXXX','Signature':'XXXXXX','Nationalidnumber':'XXXXXX','Lastname':'XXXXXX'}You can access user infromation by using keys and attributes just as shown below;>>>user_detetail['Firstname']'XXXXXX'>>>user_detail.get('Middlename')'XXXXXX'>>>user_detail.Lastname'XXXXXX'Image and Signature are currently not supported (Depreciated !!)National ID Photo and Signature are auto converted into PIL Images and you can easily save just as shown below;>>>user_detail.Photo.save('National_ID.png')>>>user_detail.Signature.save('Signature.png')If you want the data to be in the same from an API without any side effect preprocessing do this instead while loading user;>>>user_detail=load_user('xxxxxxxxxx',json=True)>>>print(user_detail){....}Give it starDid you find this repository useful to you ? Well then give it a star so as more people can get to know about it;IssuesAre you facing any issue with usage of the package, just raise an issue and I looking to fixing it as soon as I can.ContributionsIf there is anything yould would like to add warmly welcome, Just fork itDisclaimersThis is not an official package, therefore I'm not responsible for any misinformation or misuse of the package of any kind !!!CreditsAll the credits toKalebuandStackOverflow commentfromdbrax
Package: nidaba
OverviewNidaba is the central controller for the entire OGL OCR pipeline. It oversees
and automates the process of converting raw images into citable collections of
digitized texts.It offers the following functionality:Grayscale ConversionBinarization utilizingSauvolaadaptive
thresholding, Otsu, or ocropus’s nlbin algorithmDeskewingDewarpingIntegration oftesseract,kraken, andocropusOCR enginesPage segmentation from the aforementioned OCR packagesVarious postprocessing utilities like spell-checking, merging of multiple
results, and ground truth comparison.As it is designed to use a common storage medium on network attached storage
and thecelerydistributed task queue it scales
nicely to multi-machine clusters.BuildTo easiest way to install the latest stable(-ish) nidaba is from PyPi:$ pip install nidabaor run:$ pip install .in the git repository for the bleeding edge development version.Some useful tasks have external dependencies. A good start is:# apt-get install libtesseract3 tesseract-ocr-eng libleptonica-dev libleptTestsPer default no dictionaries and OCR models necessary to runs the tests are
installed. To download the necessary files run:$ python setup.py download$ python setup.py nosetestsTests for modules that call external programs, at the time only tesseract,
ocropus, and kraken, will be skipped if these aren’t installed.RunningFirst edit (the installed) nidaba.yaml and celery.yaml to fit your needs. Have
a look at thedocsif you haven’t
set up a celery-based application before.Then start up the celery daemon with something like:$ celery -A nidaba workerNext jobs can be added to the pipeline using the nidaba executable:$ nidaba batch -b otsu -l tesseract -o tesseract:eng -- ./input.tiff
Preparing filestore             [✓]
Building batch                  [✓]
951c57e5-f8a0-432d-8d77-8a2e27fff53cUsing the return code the current state of the job can be retrieved:$ nidaba status 25d79a54-9d4a-4939-acb6-8e168d6dbc7c
PENDINGWhen the job has been processed the status command will return a list of paths
containing the final output:$ nidaba status 951c57e5-f8a0-432d-8d77-8a2e27fff53c
SUCCESS
14.tif → .../input_img.rgb_to_gray_binarize.otsu_ocr.tesseract_grc.tif.hocrDocumentationWant to learn more?Read the
Docs
Package: nidcpower
Overall Statusmaster branch statusGitHub statusInfoPython bindings for NI Modular Instrument drivers. SeeGitHubfor the latest source.AuthorNIAboutThenidcpowermodule provides a Python API for NI-DCPower. The code is maintained in the Open Source repository fornimi-python.Support Policynidcpowersupports all the Operating Systems supported by NI-DCPower.It followsPython Software Foundationsupport policy for different versions of CPython.NI-DCPower Python API StatusNI-DCPower (nidcpower)Driver Version Tested Against2023 Q2PyPI VersionSupported Python VersionDocumentationOpen IssuesOpen Pull RequestsInstallationAs a prerequisite to using thenidcpowermodule, you must install the NI-DCPower runtime on your system. Visitni.com/downloadsto download the driver runtime for your devices.The nimi-python modules (i.e. forNI-DCPower) can be installed withpip:$ python -m pip install nidcpower~=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 thenidcpowermodule to open a session to a Source Meter Unit and measure voltage and current.importnidcpower# Configure the session.withnidcpower.Session(resource_name='PXI1Slot2/0')assession:session.measure_record_length=20session.measure_record_length_is_finite=Truesession.measure_when=nidcpower.MeasureWhen.AUTOMATICALLY_AFTER_SOURCE_COMPLETEsession.voltage_level=5.0session.commit()print('Effective measurement rate:{0}S/s'.format(session.measure_record_delta_time/1))samples_acquired=0print('Channel           Num  Voltage    Current    In Compliance')row_format='{0:15}{1:3d}{2:8.6f}{3:8.6f}{4}'withsession.initiate():channel_indices='0-{0}'.format(session.channel_count-1)channels=session.get_channel_names(channel_indices)fori,channel_nameinenumerate(channels):samples_acquired=0whilesamples_acquired<20:measurements=session.channels[channel_name].fetch_multiple(count=session.fetch_backlog)samples_acquired+=len(measurements)foriinrange(len(measurements)):print(row_format.format(channel_name,i,measurements[i].voltage,measurements[i].current,measurements[i].in_compliance))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: nidhichaudharygsmtpd007
Library: nidhichaudharygsmtpd007This library helps in taking the google sheets and can even convert the sheets into the pandas dataframe and then it can use this dataframe to plot it into 3 different forms of graphs:Bar plotLine Plot with markers and dotted lineScatter Plot with the mean line and the color.** Details of each functions are described belowTo call the google spreadsheet:Use createcredentials() for this purpose:createcredentials('your api json filename', 'spreadsheet to be called')Put the full path for the api json filenameIt returns the list of the dictionary from the called spreadsheet.To convert the list of dictionary returned from the createcredentials() to dataframe:Use createDF() for this purpose:createDF(d) 
      - d: the returned list of dictionaryReturns the dataframe that can be used for plotting.To plot the graphs of your choice:Currently the library provides you to plot 3 graphs: Bar Plot, Line Plot, Scatter Plot.For Barlplot you need to call:plotBar(dataframe, 'column name for the x axis', 'column name for the y axis')The function automatically plots the graph for you.For Lineplot you need to call:plotLine(dataframe, 'column name for the x axis', 'column name for the y axis')The function plots the graph which is represented by the dotted line and the points are marked by the '>' symbol.For Scatterplot you need to call:plotScatter(dataframe, 'column name for the x axis', 'column name for the y axis', colors)
  colors: Array for designing the colorbar.The function returns the Scatterplot which is represented by the green color and along with it there shall be a color bar which describes the  variation of color throughout the plotting of graph. Along with that mean is also plotted with that.Note: All figures can be easily saved.Pre-Requisites:Google API for spreadsheetsFor creating this library I have used gspread for calling the google spreadsheet api.
Package: nidhogg
Nidhogg is a wrapper interface toNetapp filersaccrossversions and technologies(sevenmode, vserver)
using the native Netapp REST API. It provides a consistent interface for the most common operations.Installing nidhoggYou can installnidhoggusing:pip install nidhoggnidhoggrequires Python >= 2.7 or Python <= 3.7.Please help outThis project is still under development. Feedback and suggestions are
very welcome and I encourage you to use theIssues liston Github to provide that feedback.Feel free to fork this repo and to commit your additions. For a list
of all contributors, please see the fileAUTHORS.txt.You will needpy.testto run the tests.License termsNidhogg is published under the permissive terms of the MIT License, see
the fileLICENSE.txt. Although the MIT License does not
require you to share any modifications you make to the source code,
you are very much encouraged and invited to contribute back your
modifications to the community, preferably in a Github fork, of
course.Usageimportnidhoggfiler=nidhogg.get_netapp("filer99.example.com","<username>","<password>")filer.create_qtree("volume_name","qtree_name")Attention: Theusernamemust be allowed to access the NetApp REST API.DocumentationLook at theAPI documentation.Planned further worksupport snapmirror methods for Netapp vserver modeadd EMC Isilon wrapperNidhogg? Is this an acronym? Or what!?No acronym, go tohttp://en.wikipedia.org/wiki/Nidhogg.If you want to use it as an acronym, use this:NiftyInterfaceDeveloped byHandsomeOdoriferousGentleGeeks
Package: nidhoggr
NidhoggrYggdrasil protocol implementationThis package provides only API and intended for developers only.DevelopmentInstallationsudoapt-getinstalllibssl-devswigpython3-devgcc
python3-mvenvvenvsourcevenv/bin/activate
pipinstall-rrequirements_dev.txt-rrequirements.txtTo run the all tests run:toxHistory0.7.1 (2020-01-09)Updated nidhoggr_core to 0.4.10.7.0 (2020-01-07)BreakingBe aware of StatusResponse returned from repository0.6.0 (2019-12-31)Used nidhoggr-coreRefactored and cleaned code0.5.0 (2019-12-31)Full compatibility with vanilla authlib0.4.0 (2019-12-26)(Breaking)Drop all texture-related features, except very basic
ones0.3.0 (2019-12-26)Various bugfixes0.2.0 (2019-12-26)(Breaking)Drop password field from User model0.1.0 (2019-12-26)First release on PyPI.
Package: nidhoggr-core
nidhoggr-coreThis package contains all domain model for [nighoggr] library for
convenient interop with other Python applications (likenidhoggr-requests).History0.5.0 (2020-01-10)BreakingAlways return User from UserRepository0.4.1 (2020-01-09)Improved API consistency0.4.0 (2020-01-09)BreakingSplit StatusResponse on two classes0.3.0 (2020-01-06)BreakingReturn StatusResponse from each method of repositories0.1.0 (2019-12-31)First release on PyPI.
Package: nidhoggr-requests
Nidhoggr-requestsRequests-based repository implementation for nidhoggrDevelopmentInstallationsudoapt-getinstalllibssl-devswigpython3-devgcc
python3-mvenvvenvsourcevenv/bin/activate
pipinstall-rrequirements_dev.txt-rrequirements.txtTo run the all tests run:toxHistory0.5.0 (2020-01-10)BreakingCompatibility with nidhoggr_core 0.5.x0.4.0 (2020-01-09)BreakingCompatibility with nidhoggr_core 0.4.x0.3.0 (2020-01-06)BreakingCompatibility with nidhoggr_core 0.3.00.2.0 (2019-12-31)Used nidhoggr-core0.1.0 (2019-12-31)First release on PyPI.
Package: nidmd
Dynamic Mode DecompositionBased onCasorso et al., 2019, the dynamic mode decomposition (DMD) algorithm allows for a dynamic analysis of cortical neurological activation. Here, a Python module is developed facilitating both analysis and visualization aspects of the DMD.InstallationTo install the package, simply run the following command::pip install nidmdUsageDashboardIn parallel to this Python module, a dashboard callednidmd-dashboardhas been developed to facilitate analysis, comparison, and mode matching of the DMD of time-series fMRI data.Input dataThis dashboard handles preprocessed data as described inCasorso et al., 2019 - Methods.
The input needed for a successful visualization is one or multiple files containing time-series data. Each file corresponds to an fMRI run and should contain one matrix of sizeN x T, withNbeing the number of ROIs in the cortical parcellation andTbeing the observational timepoints.In the current version, two parcellations are supported:Glasser et al., 2016, containingN = 360regions.Schaefer et al., 2018, containingN = 400regions.ExamplesA Jupyter Notebook can be found in theexamplesdirectory. It complements thedocumentation.References1M. F. Glasser et al., “A multi-modal parcellation of human cerebral cortex,” Nature, vol. 536, no. 7615, pp. 171–178, 11 2016, doi: 10.1038/nature18933.2J. Casorso, X. Kong, W. Chi, D. Van De Ville, B. T. T. Yeo, and R. Liégeois, “Dynamic mode decomposition of resting-state and task fMRI,” NeuroImage, vol. 194, pp. 42–54, Jul. 2019, doi: 10.1016/j.neuroimage.2019.03.019.3A. Schaefer et al., “Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI,” Cerebral Cortex, vol. 28, no. 9, pp. 3095–3114, Sep. 2018, doi: 10.1093/cercor/bhx179.
Package: nidmfsl
NIDM-Results for FSLExport mass-univariate neuroimaging results computed in FSL (using FEAT) as NIDM-Results packs.ANIDM-Results packis a compressed file containing a NIDM-Results serialization and some or all of the referenced image data files in compliance withNIDM-Results specification.Usageusage: nidmfsl [-h] [-g GROUP_NAME NUM_SUBJECTS] [-o OUTPUT_NAME] [-d]
               [-n NIDM_VERSION] [--version]
               feat_dir

NIDM-Results exporter for FSL Feat.

positional arguments:
  feat_dir              Path to feat directory.

optional arguments:
  -h, --help            show this help message and exit
  -g GROUP_NAME NUM_SUBJECTS, --group GROUP_NAME NUM_SUBJECTS
                        Group label followed by number of subjects
  -o OUTPUT_NAME, --output_name OUTPUT_NAME
                        Name of the output. A ".nidm.zip" or ".nidm" (when -d
                        is used) suffix will be appended.
  -d, --directory-output
                        Produces a .nidm directory rather than a .nidm.zip
                        file.
  -n NIDM_VERSION, --nidm_version NIDM_VERSION
                        NIDM-Results version to use (default: latest).
  --version             show program's version number and exitInstallationTo install, run the below command in the bash terminal.pip install nidmfslCompatible withFSL version 5.0.9TestingRequirementsTo run the tests for this repository, the following must be installedGit LFS. Installation instructions for Git LFS can be foundhere.The python packagesvcrandddt. These can be installed using the below commands in the bash terminal:pip install vcrpy
pip install ddtIn addition, the test data must also be downloaded from thenidmresults-examplesrepository to<path_to_this_repository>/test/data/nidmresults-examples.git lfs clone https://github.com/incf-nidash/nidmresults-examples.git <path_to_this_repository>/test/data/nidmresults-examplesRunning the testsThe below command can be used to generate the test cases.python <path_to_this_repository>/test/export_test_battery.pyFolowing this, the test cases can be verified against the ground truth provided in thenidmresults-examplesrepository using the below command.cd <path_to_this_repository>/test/
python -m unittest discover
Package: nidmm
Overall Statusmaster branch statusGitHub statusInfoPython bindings for NI Modular Instrument drivers. SeeGitHubfor the latest source.AuthorNIAboutThenidmmmodule provides a Python API for NI-DMM. The code is maintained in the Open Source repository fornimi-python.Support Policynidmmsupports all the Operating Systems supported by NI-DMM.It followsPython Software Foundationsupport policy for different versions of CPython.NI-DMM Python API StatusNI-DMM (nidmm)Driver Version Tested Against2023 Q1.1PyPI VersionSupported Python VersionDocumentationOpen IssuesOpen Pull RequestsInstallationAs a prerequisite to using thenidmmmodule, you must install the NI-DMM runtime on your system. Visitni.com/downloadsto download the driver runtime for your devices.The nimi-python modules (i.e. forNI-DMM) can be installed withpip:$ python -m pip install nidmm~=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 thenidmmmodule to open a session to a DMM and perform a 5.5 digits of resolution voltage measurement in the 10 V range.importnidmmwithnidmm.Session("Dev1")assession:session.configure_measurement_digits(nidmm.Function.DC_VOLTS,10.0,5.5)print("Measurement: "+str(session.read()))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: nido
This respository is the primary library that the Nido smart thermostat is built around. If you're looking for instructions on how to run Nido on a Raspberry Pi, seehttps://github.com/alexmensch/nidofor instructions and the full project background.Running the application locally for developmentRequirements and initial configurationNido has been tested with Python 3.6.5 and higher. Python 2 is not supported.You will need bothDocker and Docker Composeinstalled locally.Renameprivate-config.py.exampletoprivate-config.pywith your own private settings.Starting the Nido backend and API server> docker-compose upRun Docker Compose from the base of the project to run the Nido thermostat and API locally. If you are not running on a Raspberry Pi, a test hardware fixture will be loaded instead of the native Raspberry Pi GPIO library.Docker local volume mappingsnido/Local changes to the package source code are mapped to the Docker containers.instance/Nido settings and scheduler database entries are stored outside the container.log/Logs generated by backend RPC service and scheduler are generated here.Shutting down Nido> docker-compose downScripts and development toolsclean.shDelete all local and cached files generated by running in your local environment.build.shBuild the Nido package for distribution.pip install -r dev_requirements.txtInstalls packages useful for local development. Using Pythonvenvis recommended.