Saravanan666/pypi-org huggingface dataset:
Package: nsforest
NS-ForestGetting StartedDownload NSForest_v3dot9_1.py.PrerequisitesThis is a python script written and tested in python 3.8, scanpy 1.8.2, anndata 0.8.0.Other required libraries: numpy, pandas, sklearn, itertools, time, tqdm.TutorialFollow thetutorialto get started.Versioning and citationsThis is version 3.9.1. Earlier releases are managed inReleases.Version 2 and beyond:Aevermann BD, Zhang Y, Novotny M, Keshk M, Bakken TE, Miller JA, Hodge RD, Lelieveldt B, Lein ES, Scheuermann RH. A machine learning method for the discovery of minimum marker gene combinations for cell-type identification from single-cell RNA sequencing. Genome Res. 2021 Jun 4:gr.275569.121. doi: 10.1101/gr.275569.121.Version 1.3/1.0:Aevermann BD, Novotny M, Bakken T, Miller JA, Diehl AD, Osumi-Sutherland D, Lasken RS, Lein ES, Scheuermann RH. Cell type discovery using single-cell transcriptomics: implications for ontological representation. Hum Mol Genet. 2018 May 1;27(R1):R40-R47. doi: 10.1093/hmg/ddy100.AuthorsYun (Renee) Zhangzhangy@jcvi.orgRichard ScheuermannRScheuermann@jcvi.orgBrian Aevermannbaevermann@chanzuckerberg.comLicenseThis project is licensed under theMIT License.AcknowledgmentsBICCNAllen Institute of Brain ScienceChan Zuckerberg InitiativeCalifornia Institute for Regenerative Medicine
Package: nsfw
python-nsfwA Python package for analyzing images usingCaffeandYahoo's open_nsfwmodels.Prerequisitespython-nsfw requires Python 3 and runs on Caffe-compatible systems.CaffeIf you're using macOS andHomebrew, the easiest way to installCaffeis with Homebrew:brewinstallcaffeAlternatively, you could use aDockerLinux image (e.g.debian:stretch-slim) and install the necessary dependencies:aptupdate&&aptinstallcaffe-cpupython3python3-pipwgetInstallationpython-nsfw may be installed usingpip:pip3installnsfwUsageimportPIL.ImageasImagefromnsfwimportclassifyimage=Image.open("/path/to/image.jpg")sfw,nsfw=classify(image)print("SFW Probability:{}".format(sfw))print("NSFW Probability:{}".format(nsfw))Licensepython-nsfw is freely available under theMIT License. Use it, learn from it, fork it, improve it, change it, tailor it to your needs.python-nsfw ships with copies of Yahoo'sopen_nsfw Caffe models(nsfw/deploy.prototxtandnsfw/resent_50_1by2_nsfw.caffemodel) which are licensed under theBSD 2-Clause License.
Package: nsfw-dl
nsfw_dlThis is an discord library that works with aiohttp and async.It uses Python 3.5+ syntax.DocumentationThere is no documentation as of yet for usage ofnsfw_dl.Known IssuesIt is known thatdrunkenpumkendoes not have an working search (I
need to figure out how to send an tag to the api if they have one to get
the same result as most of the other search classes).Also I could not figure out an search fornhentai,tsumino, andhbrowse.If you know how to do it for search terms / tags that would help finish
this library.
Package: nsfwrecog
NSFW RecogThis library is meant to detect and censor nsfw content in public media. Models can be foundhere, they are downloaded automatically and cached.Installationinstall requirements.txtinstall pytorch according to your cuda version(optional) install onnxruntime-gpu to support cuda usageUsageInstantiate a NsfwDetector class and use the methods detect(), blur(), video() and camera(). The demos show the usage:demo_detect.py: Detecting body parts/objects in an imagedemo_camera.py: Live detecting objects in a webcam streamdemo_video.py: Detecting objects in a video and draw bounding boxes and labels to a new videodemo_blur.py: Detecting objects in an image and blurring them.
Package: nsfw-screen-detect
NSFW Screen DetectA small python package for detecting NSFW content on a Windows display.UsageThis package does the following by default:Take a screenshot on all connected displays. You can take a screenshot of just the active window by passinginput='active_window'to thecheckmethod. You can also pass in a Pillow image like so :input=ImageDetect actual images from the screenshot. You can skip this by passingparse_images=Falseto thecheckmethod.Run skin detection. You can set theskin_thresholdparameter to have NSFW checking skipped if an image contains very little skin.Use AI to detect how NSFW each image is. You can passcheck_type='skin'to just get a skin color score.Returns each analyzed picture along with its results.Examples# Import and initialize the main Checker class
from nsfw_screen_detect import Checker
checker = Checker()

# Run check on all monitors
results = checker.check()

# Run check on active window
results = checker.check(input='active_window')

# Run check for skin content
results = checker.check(check_type='skin')

# Run check without parsing real pictures from screenshot
results = checker.check(parse_images=False)Big thanks to NudeNet for their AI model.
Package: nsgcli
NetSpyGlass Command Line Toolsthis package installs Python modulensgcliand two command line scripts:nsgcliandnsgqlthat
use itUse scripttools/build.shto build andtools/upload.shto push to pypiExample of API call made bynsgclito nsg-api service:System statuscurl-H"X-NSG-Auth-API-Token:$NSG_API_TOKEN"$NSG_SERVICE_URL/v2/nsg/cluster/net/1/statusNSGQLcurl-d'{"targets": [{"format":"table", "nsgql":"select count(key) from alerts"}]}'-XPOST-H"X-NSG-Auth-API-Token:$NSG_API_TOKEN"$NSG_SERVICE_URL/v2/query/net/1/data/NSGGROKParse text with custom patternnsggrok--pattern"hello world of %{WORD:world_name}"text"hello world of Grok"{"world_name":"Grok"}Parse syslog message with built-in patternsnsggroklog"<13>May 18 11:22:43 carrier sshd: SSHD_LOGIN_FAILED: Login failed for user 'root' from host '10.1.1.1'"{"sshUser":"root","index":"labdcdev-syslog-short","sshSrcIp":"10.1.1.1","logText":"SSHD_LOGIN_FAILED: Login failed for user 'root' from host '10.1.1.1'","prio":"13","logSyslogSeverityName":"notice","logSource":"carrier","logSyslogText":"<13>May 18 11:22:43 carrier sshd: SSHD_LOGIN_FAILED: Login failed for user 'root' from host '10.1.1.1'","logSyslogFacilityName":"user","logTimestamp":"2021-05-18T11:22:43.000Z","program":"sshd","logSyslogPriority":13,"timestamp":"May 18 11:22:43","logSyslogFacilityCode":1,"logSyslogSeverityCode":5}Meraki API callcurl-G-H"X-NSG-Auth-API-Token:$NSG_API_TOKEN"$NSG_SERVICE_URL/v2/nsg/cluster/net/1/exec/api--data-urlencode'region=world'--data-urlencode'url=https://api.meraki.com/api/v1/organizations/626563298157920259/devices'--data-urlencode'method=GET'--data-urlencode'args=gap-meraki'Test connection to remote servercurl-G-H"X-NSG-Auth-API-Token:$NSG_API_TOKEN"$NSG_SERVICE_URL/v2/nsg/cluster/net/1/exec/connect--data-urlencode'region=world'--data-urlencode'args=10.210.24.112 9339 1000'Set log levelcurl-G-H"X-NSG-Auth-API-Token:$NSG_API_TOKEN"$NSG_SERVICE_URL/v2/nsg/cluster/net/1/exec/set_log_level--data-urlencode'region=world'--data-urlencode'args=tme-server-15v io.grpc DEBUG'Send PING command to an agentcurl-L-H"X-NSG-Auth-API-Token:$NSG_API_TOKEN"$NSG_SERVICE_URL/apiv3/net/1/exec/ping/agent/vkhome?address=127.0.0.1
Package: nsgi
nsgiThe newest contender in Server Gateway Interface.Why use this webserver?This webserver is made with the newest version ofasyncio, andsockets, it supports callablle-based interface and also class based. Not enough? Let me tell you how this project works.How it worksBasically, you create an application, let's say, an API, you run the api with ourRunnerclass and want to visit it, you go tohttp://development.com(Go to your webserver IP), you will receive a response wich was triggerred from theResponseyou gave in the code.It's as simple as it sounds.Example:Want an example? Here:fromnsgi.applicationimportAsyncServerfromnsgi.runnerimportRunnerfromnsgi.responsesimportResponseclassServer(AsyncServer):def__init__(self)->None:super().__init__()server=Server()runner=Runner(server)@server.get("/",method="GET")asyncdefindex(request):response=Response("Hello.")returnawaitresponse()# or return responserunner.run()
Package: nsglib
NSG Common LibraryNSG common library.A Python 3 library that contains the basic objects and schemas that NSG tile
caches, or NSG components would need to adhere to.MotivationWe wanted a library to contain common geospatial objects and commonly used
functions to be used acrossed NSG python geospatial libraries and tools.Build statusSample CodeExample of calculating resolution width and height:fromnsg.common.spatial_reference_systemimportSpatialReferenceSystemfromnsg.tile_cache.tile_schemeimportcalc_resolutionsresolution_width,resolution_height=calc_resolutions(bounding_box=SpatialReferenceSystem.NSG_WORLD_GEODETIC.world_bounding_box,tile_size=(256,256),zoom_level=1,tile_scheme=SpatialReferenceSystem.NSG_WORLD_GEODETIC.tile_scheme)Installationpip install nsglibCommon NSG objectsSome examples of NSG objects include (but not limited to):SpatialReferenceSystemBoundingBoxCoordinateThe most commonSpatial Reference Systemsthat are defined areEPSG:4326, EPSG:3395, andEPSG:3857.
These Spatial Reference Systems can be easily accessed by using the Python Enum:SpatialReferenceSystem.NSG_WORLD_GEODETIC,SpatialReferenceSystem.NSG_WORLD_MERCATOR,SpatialReferenceSystem.WEB_MERCATORThebounding boxdefines an object that holds the extent information in a
particular Coordinate Reference System.ACoordinateis an geographical location on a map. They can be used for
Coordinate Reference System coordinates or tile coordinates.Tile Cache NSG ObjectsSome examples of Tile Cache objects include(but not limited to):TileTileSchemeAbstractTileCacheATilehas a coordinate location (row, colum, zoom level) and a source.
The coordinate location describes where to place the tile.
The source is the tile data in bytes (the jpeg, png, or vector-tile data).ATile Schemeis how the world is divided up into tiles based on zoom level.
There is also an implementation included calledZoomTimesTwowhich is a tile
scheme implementation of a common convention where a tile set's tile matrix
doubles width and height between successive zoom levels.Tile Schemeclass also includes functions for calculating the resolution and
scale denominator.AnAbstractTileCacheare the basic functions that a tile cache should include.
Some example tile caches areCompact Cache V2,GeoPackage, andFile based Cache.LicenseMIT
Package: nsg-parts-search
NSG Parts SearchA class used for querying parts in Octopart usingNexar API v4.0.The following class has all the needed methods required by NSG to create a query toOctopart.This package is maintained and used byNSG, please refer to the licensing before using.Table of contentInstallationPrerequisitesUsageResponseLicenseInstallationUse the package managerpipto installnsg-parts-search.pipinstallnsg-parts-searchPrerequisitesUse the application client ID and secret. Refer to theNSG Documentation.Set environment variablesCLIENT_IDandCLIENT_SECRET.UsageGeneral example to get a response from a list of parts search:fromnsgsearchimport*# set credentialsclientId=os.environ['CLIENT_ID']clientSecret=os.environ['CLIENT_SECRET']# start the clientsearch=NSG_OctopartSearch()search.setCredentials(clientId,clientSecret)search.startClient()# search for partsparts_list=[]results=search.partsSearch(parts_list)print(results)Filtering FunctionsSay that from a list of parts you want a table of the Manufacturer, Suppliers, Quantity and Price per unit in USD:# loop through all partsforpartinparts_list:# enter just if the part number was foundifresults[part]['found']:part_data=results[part]['data']# get a table of prices for the partpriceTable=search.partPricesTable(part_data)Maybe now you want to get the lowest price from the table for that part number:defget_lowest_price(table):price=table[0]['price']supplier=str()qty=int()forrowintable:ifrow['price']<price:price=row['price']supplier=row['supplier']qty=row['reqPurchase']return([supplier,qty,price])Now let's say you want to get the lead times for all suppliers and filter your prices to be no more than 30 days old:ResponseThe response from the search is a dictionary containing all the requested part numbers as keys.Each key contains another dictionary with the keysfoundanddataand have the following format:{"PN#":{"found":True/False,"data":{...}},{...}}The default query response has the following format:part{mpnmanufacturer{name}shortDescriptionspecs{attribute{namegroup}displayValue}octopartUrlsimilarParts{mpnshortDescriptionmanufacturer{name}octopartUrlcategory{name}}companionProducts{part{mpnshortDescriptionmanufacturer{name}octopartUrlcategory{name}}}category{name}bestDatasheet{url}countsmedianPrice1000{price}sellers{company{name}countryoffers{skuinventoryLevelmoqprices{quantitypricecurrency}clickUrlupdatedfactoryLeadDays}isRfq}}You can change the query response by using the method changeQuery():newResponse='''query Search($mpn: String!) {supSearchMpn(q: $mpn, limit: 1) {results {part {mpnshortDescriptionmanufacturer {name}}}}}'''search.changeQuery(newResponse)API Part Referencehttps://octopart.com/api/v4/reference#partLicenseGNU LGPL
Package: nsgptorch
NSGP-TorchExamplesgpytorchmodel withskgpytorch# Import packagesimporttorchfromregdataimportNonStat2Dfromgpytorch.kernelsimportRBFKernel,ScaleKernelfromskgpytorch.modelsimportExactGPRegressorfromskgpytorch.metricsimportmean_squared_error# Hyperparametersn_iters=100# Load datadatafunc=NonStat2D(backend="torch")X_train,y_train,X_test=map(lambdax:x.to(torch.float32),datafunc.get_data())y_test=datafunc.f(X_test[:,0],X_test[:,1]).to(torch.float32)# Define a kernelkernel=ScaleKernel(RBFKernel(ard_num_dims=X_train.shape[1]))# Define a modelmodel=ExactGPRegressor(X_train,y_train,kernel,device='cpu')# Train the modelmodel.fit(n_iters=n_iters,random_state=seed)# Predict the distributionpred_dist=model.predict(X_train,y_train,X_test)# Compute RMSE and/or NLPDmse=mean_squared_error(pred_dist,y_test,squared=False)nlpd=neg_log_posterior_density(pred_dist,y_test)nsgptorchmodel withskgpytorch# Import packagesimporttorchfromregdataimportNonStat2Dfromnsgptorch.kernelsimportrbffromskgpytorch.modelsimportExactNSGPRegressorfromskgpytorch.metricsimportmean_squared_error# Hyperparametersn_iters=100# Load datadatafunc=NonStat2D(backend="torch")X_train,y_train,X_test=map(lambdax:x.to(torch.float32),datafunc.get_data())y_test=datafunc.f(X_test[:,0],X_test[:,1]).to(torch.float32)# Define a kernel list for each dimensionkernel_list=[rbf,rbf]# Define inducing points for each dimension (must be none if not applicable)inducing_points=[None,None]# Define a modelmodel=ExactNSGPRegressor(kernel_list,input_dim=2,inducing_points,device='cpu')# Train the modelmodel.fit(X_train,y_train,n_iters=n_iters,random_state=seed)# Predict the distributionpred_dist=model.predict(X_train,y_train,X_test)# Compute RMSE and/or NLPDmse=mean_squared_error(pred_dist,y_test,squared=False)nlpd=neg_log_posterior_density(pred_dist,y_test)PlanEach kernel is 1DMultiply kernels to each otherIdeasCompute distance once and save itUpdate skgpytorch to use 1 std instead of 0.1Do something about mean learning of gpytorch for comparison
Package: nsg-python-sdk
nsg-python-sdkThis repository contains changes for nsg-python-sdk python package. This package will be uploaded to the PyPI so that customer can install package and test their changes.nsg-python-sdk packageInstall nsg-python-sdk from PyPI. This package exposes nsgsdk binary.pip install nsg-python-sdkcurrently nsgsdk supports running tags, variables and views app request. Supported apps can be found by running the following command.# nsgsdk -hUsage:nsgsdk[OPTIONS]COMMAND[ARGS]...

Options:--versionShowtheversionandexit.-h,--helpShowthismessageandexit.

Commands:tagsruntagsapplicationtestruntestsinthecurrentrepositoryvariablesrunvariablebuilderapplicationviewsrunviewsapplicationHow to run apps on local machineGo to the directory which contains scripts for testing.To run moduletagsusing embeddedtags_app.py, we could use the following stepsto check the required parameters and options we can use the help option# nsgsdk tags -hUsage:nsgsdktags[OPTIONS]INPUT_DEVICENetSpyGlasstagsassignmentPythonapplication

Options:-f,--format[json|pb]usethisformattogenerateoutput[default:json]-o,--outputTEXTthefilenamewheretheresultshouldbestored;ifmissingorequalto"-",usestdout-h,--helpShowthismessageandexit.tagscommand specifies that we want to run the tags app.then to run it with inputnsgsdktags<path_to_input_file>-o<path_to_output_file>Run unit test cases.cd to the scripts directory.create directorytestsand put your test modules there. If your tests need data files, we recommend
placing them in the directorytests/fixtures. Directory structure should look like this:~/src/test-repo/scripts$ tree
    ├── __init__.py
    ├── rules.py
    ├── tags.py
    ├── tests
    │     ├── __init__.py
    │     ├── fixtures
    │     │     └── ex2200-pb.json
    │     └── test_tags.py
    ├── var_builder.py
    └── views.pyto run the tests, change to the directory which contains scripts directory. We can use the following commandnsgsdk testTo check the required parameters and option we can use the following command:nsgsdk test -h
Usage: nsgsdk test [OPTIONS] PATH

Options:
  -h, --help  Show this message and exit.here Path can be module or directory path.To run the tests in the directory, we can use the following command:nsgsdk test scripts/testsTo run the tests in module, we can use the following command:nsgsdk test scripts/tests/test_tags_1.py
Package: nshare-calc
nshare-calcPython package for calculating the optimal number of shares given a portfolio using integer programming.Problem statementGiven a portfolio with total asset $S$, price vector $\mathbf p$ and weight vector $\mathbf w$, find integer vector $\mathbf n$ such thatThe total value of all stocks must not exceed the total asset, i.e., $\mathbf p \cdot \mathbf n \leq S$;Minimize the total absolute difference between the desired values and the actual values for all stocks, i.e., $\Sigma_i |S w_i - p_i n_i|$.Installationpipinstallnshare-calcUsagefromnshare_calcimportcalc_n# paramsS=100p=[6,5.5]w=[0.6,0.4]# w will be automatically normalizedn=calc_n(S,p,w)# Result: n = [10.  7.]
Package: nshm-toshi-client
nshm-toshi-clientA python3 client for the nshm-toshi-api.Documentation:https://gns-science.github.io/nshm-toshi-clientGitHub:https://github.com/gns-science/nshm-toshi-clientPyPI:https://pypi.org/project/nshm-toshi-client/Free software: MITFeaturesTODO
Package: nshowrss
Ncurses interface for downloading showrss.info series, and keeping track of the watched / not watched episodesFree software: BSD licenseDocumentation:https://nshowrss.readthedocs.org.FeaturesNCurses interface for showrss.info using libtorrent streamingNCurses interface for IMDB + KAT using libtorrent streamingNCurses interface for direct magnet links lists (just magnets, one at a line)Usageusage: nshowrss [-h] [--showrss_url [SHOWRSS_URL]] {imdb,showrss,magnet}

NShowRSS

positional arguments:
  {imdb,showrss,magnet}
                        Interface

optional arguments:
  -h, --help            show this help message and exit
  --showrss_url [SHOWRSS_URL]
                        Showrss rss urlShowRSSThis uses a showrss.info RSS feed to build an interface and stream.MagnetGiven a list of magnets, it’ll try to discover its series title/season/chapter and
build an interface for it, with streaming support too.IMDBGets the most popular IMDB shows and builds an interface with them.
Tries to look the episodes on KAT (looks magnet links) once selected and streams it.History0.1.2DocumentationFix excessive debugging info on default interface and help0.1.1Updated pychapter version to support automatic torrent searchingAdded support forIMDB interfaceMagnet links (via pipe) interfaceShowRSS.info interface0.1.0 (2015-01-11)First release on PyPI.
Package: nsi.granulate
Introduction============The Granulate packageRequirements============- python-imaging- pypdf2table- evince-thumbnailer(evince)- python-opencv(apt-get install python-opencv)Installation============easy_install -f http://pascal.cefetcampos.br/pypi nsi.granulateChangelog=========0.10.1 - Release-------------* Corrigido o bug com imagens retornadas vazias pelo processo de granularização0.10.0 - Release-------------* Adicionada nova politica de segmentação. Retirada a dependência do gst e adicionado o opencv0.9 - Release-------------* Caching de arquivos convertidos adicionado0.8 - Release-------------* Adicionado suporte pra thumbnails de .docx e .pptx0.7.2 - Release---------------* Adicionado granularizacao de pptx0.7.1 - Release---------------* Adicionado testes para outros formatos de videos0.7 - Release-------------* Adicionado granularizacao de docx0.6 - Release-------------* Acrescentado algoritmo de descarte de imagens repetidas para arquivos PDF0.5 - Release-------------* Remove Dependencie pymedia and include Dependencie gstreamer0.1 - Unreleased----------------* Remove Dependencie ImageMagick* Initial release
Package: nsii
Simple CLI for pythonThis CLI uses only native python libraries, which means that you don't need to use "pip install" in your projects, but, it is only implemented and tested on Windows.Features.ppm image supportsupport for simple geometrysupport for the textDocumentationSee documentation.pyDemohttps://user-images.githubusercontent.com/101042422/178258748-15366975-d090-4421-b92e-b876581c85a7.mp4Authors@bruh
Package: nsi.metadataextractor
Introduction=====**nsi.metadataextractor** is a metadata extractor for academic (Portuguese-BR) documents like:```Course Conclusion (ABNT format)Event ArticlePeriodic ArticleSupported extention: .pdf```Setup=====pip install nsi.metadataextractorExample=====**Python**from nsi.metadataextractor.extractors import tcc, event, periodicpath = "/home/stuff/tccdocument.pdf"tccextractor = tcc.TccExtractor(path)eventextractor = event.EventExtractor(path)periodicextractor = periodic.PeriodicExtractor(path)tccextractor.all_metadata()eventextractor.all_metadata()periodicextractor.all_metadata()**Bash**>>> extract_metadata /home/stuff/tccdocument.pdf -t tcc>>> extract_metadata /home/stuff/eventdocument.pdf -t event>>> extract_metadata /home/stuff/periodicdocument.pdf -t periodic
Package: nsingleton
NSingleton:star2:Features| :pushpin:What and why| :gear:Run and configuration| :scroll:LicenseNew Singleton decorator. Just another singleton pattern implementation. Thread-safe, with type casting.:star2: FeaturesYet another singleton decoration implementation. This one just works;Thread-safe decorator?! I'm not really sure it works or is needed, but let it be;Type casting! VSCode's linters are not screaming and auto-completion works as expected;:pushpin: What and whyJust wanted to push my singleton as a library to be able to reuse it in my projects. Going to support it as well. Please do not hesitate to ask questions or make suggestions if anything.:gear: Run and configurationInstall library with PyPi, import and use.:scroll: LicenseDistributed under theMIT Licenselicense. See LICENSE for more information.
Package: nsipptparser
Very simple package for parsing .nsippt files to json. Use Python 3.4 and up.Usagepygments_style='sphinx'importnsipptparserimportjsonwithopen("sneaky.nsippt")asfile:data=nsipptparser.parse(file)# Get the first length measurement of the first Viewlength=data["ViewPropertiesSet"]["View Properties"][0]["Actions Set"]["Length"][0]["length value"]print("The length is ",length)# Get the base64 thumbnail of the first Viewthumbnail=data["ViewPropertiesSet"]["View Properties"][0]["Thumbnail"]print("The thumbnail is ",thumbnail)# Save to json filewithopen('data.json','w')asf:json.dump(data,f,indent=4)Note: All values are parsed as strings, so you will have to parse the strings further yourself.InstallationpipinstallnsipptparserRequirementsCompatibilityLicenceMITAuthorsnsipptparserwas written byRussell Tran.