Saravanan666/pypi-org huggingface dataset:
Package: niscope
Overall Statusmaster branch statusGitHub statusInfoPython bindings for NI Modular Instrument drivers. SeeGitHubfor the latest source.AuthorNIAboutTheniscopemodule provides a Python API for NI-SCOPE. The code is maintained in the Open Source repository fornimi-python.Support Policyniscopesupports all the Operating Systems supported by NI-SCOPE.It followsPython Software Foundationsupport policy for different versions of CPython.NI-SCOPE Python API StatusNI-SCOPE (niscope)Driver Version Tested Against2023 Q1.1PyPI VersionSupported Python VersionDocumentationOpen IssuesOpen Pull RequestsInstallationAs a prerequisite to using theniscopemodule, you must install the NI-SCOPE runtime on your system. Visitni.com/downloadsto download the driver runtime for your devices.The nimi-python modules (i.e. forNI-SCOPE) can be installed withpip:$ python -m pip install niscope~=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 theniscopemodule to open a session to a High Speed Digitizer and capture a single record of 1000 points.importniscopewithniscope.Session("Dev1")assession:session.channels[0].configure_vertical(range=1.0,coupling=niscope.VerticalCoupling.AC)session.channels[1].configure_vertical(range=10.0,coupling=niscope.VerticalCoupling.DC)session.configure_horizontal_timing(min_sample_rate=50000000,min_num_pts=1000,ref_position=50.0,num_records=5,enforce_realtime=True)withsession.initiate():waveforms=session.channels[0,1].fetch(num_records=5)forwfminwaveforms:print('Channel{0}, record{1}samples acquired:{2:,}\n'.format(wfm.channel,wfm.record,len(wfm.samples)))# Find all channel 1 records (Note channel name is always a string even if integers used in channel[])chan1=[wfmforwfminwaveformsifwfm.channel=='0']# Find all record number 3rec3=[wfmforwfminwaveformsifwfm.record==3]If you need faster fetch performance, or to directly interface withSciPy, you can use thefetch_into()method instead offetch(). See the fetch_into example.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: nise
Overall Statusmaster branch statusGitHub statusInfoPython bindings for NI Modular Instrument drivers. SeeGitHubfor the latest source.AuthorNIAboutThenisemodule provides a Python API for NI Switch Executive. The code is maintained in the Open Source repository fornimi-python.Support Policynisesupports all the Operating Systems supported by NI Switch Executive.It followsPython Software Foundationsupport policy for different versions of CPython.NI Switch Executive Python API StatusNI Switch Executive (nise)Driver Version Tested Against2023 Q1PyPI VersionSupported Python VersionDocumentationOpen IssuesOpen Pull RequestsInstallationAs a prerequisite to using thenisemodule, you must install the NI Switch Executive runtime on your system. Visitni.com/downloadsto download the driver runtime for your devices.The nimi-python modules (i.e. forNI Switch Executive) can be installed withpip:$ python -m pip install nise~=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 thenisemodule to open a session to a Switch Executive Virtual Device and connect a routegroup.importnisewithnise.Session('SwitchExecutiveExample')assession:session.connect('DIOToUUT')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: nishant-miss-data-76
Handling Missing DataThis package has been created based on Project 3 of course UCS633(DATA ANALYSIS AND VISUALISATION ) Thapar University, Patiala
Nishant Goel  
COE17
Roll number: 101703376Output is a DataFrame with allNaNremovalInstallationpip install nishant_missing_data_76Recommended - test it out in a virtual environment.Upgradepip install nishant_missing_data_76 --upgradeTo use via command linemissing_data_cli_76 in.csv out.csvDrop rows with NaNmissing_data_cli_76 in.csv out.csv DROP 0Drop columns with NaNmissing_data_cli_76 in.csv out.csv DROP 1Forward fillingmissing_data_cli_76 in.csv out.csv FILL 0Backward fillingmissing_data_cli_76 in.csv out.csv FILL 1Imputing with meanmissing_data_cli_76 in.csv out.csv IMPUTE 0Imputing with medianmissing_data_cli_76 in.csv out.csv IMPUTE 1Imputing with modemissing_data_cli_76 in.csv out.csv IMPUTE 2To use in .py scriptfrom nishant_miss_data_76 import dropvalue,filler,imputer
df=pd.read_csv('file1.csv')nd=dropvalue(df,0)nd=dropvalue(df,1)nd=imputer(df,0)//meannd=imputer(df,1)//mediannd=imputer(df,2)//modend=filler(df,along=0)nd=filler(df,along=1)
Package: nishant-outlier-76
Library for removing outliers from pandas dataframePROJECT 2, UCS633 - Data Analysis and Visualization
Nishant Goel  
COE17
Roll number: 101703376Takes two inputs - filename of input csv, intended filename of output csv.Output is the number of rows removed from the input dataset.It also shows new dataset in case of IQROutput is the number of rows removed from the input dataset in case of z-scoreInstallationpip install nishant_outlier_76Recommended - test in a virtual environment.Use via command lineoutliers_cli in.csv out.csv
outliers_cli in.csv out.csv 1.5First argument after outcli is the input csv filename from which the dataset is extracted. The second argument is for storing the final dataset after processing.Use in .py scriptfrom nishant_outlier_76 import remove_outliers_iqr
remove_outliers('input.csv', 'output.csv')from nishant_outlier_76 import remove_outliers
remove_outliers('input.csv', 'output.csv',threshold)
Package: nissaga
Nissaga, genealogy tree generatorAboutNissaga can be used to generate Genealogy trees
from a simple data description inYAML.Nissagais a Catalan word forlineage.InstallationpipinstallnissagaYou need to installgraphviz. Not the python wrapper library, but the program itself.UsageThe command line interface is still quite simple and
future versions will evolve it to a more powerful one.
Right now it works like this:nissagamyfamily.yamlThis generates a filemyfamily.pdfYou can specify a second parameter to choose the output format: png, svg or any supported by graphviz.nissagamyfamily.yamlsvgInput fileThe input file follows thisSchemadocumentedhereA simple example:people:me:fullname:Alférez Boye, Carmenborn:1988-06-12sis:fullname:Alférez Boye, Dianaborn:1985-07-04dad:fullname:Alférez Esquerra, Franborn:1957-03-23mom:fullname:Boye Gener, Hipatiaborn:1951-10-28dadgranpa:fullname:Boye Isidro, Javierborn:1932-07-22died:2013-03-19dadgranma:fullname:Gener Krahe, Lauraborn:1933-02-12died:2017-09-19momgranpa:fullname:Alférez Miró, Natanaelborn:1944-04-15momgranma:fullname:Esquerra Olivera, Pandoraborn:1931-01-13died:2007-05-19families:-parents:[mom,dad]children:[sis,me]-parents:[dadgranma,dadgranpa]children:[dad]-parents:[momgranma,momgranpa]children:[mom]Differences with kingraphThis application was started as a clone of the functionality ofkingraphto overcome performance and scalability problems with big trees,
and also to extend its functionality.So, it is mostly back compatible with kingraph format.The following kingraph features are not supported:family.children2andfamily.parents2relations are not yet supportedThe following features have been introduced by nissaga, and are not available in kingraph (but the yaml would be still compatible).Richperson boxeswith dates, photos and separated first name and surname.person.bornmaybe set to a date, and will appear as*YYYY-MM-DDbellow the person's name.person.diedmaybe set to a date or true and will appear as+YYYY-MM-DDbellow the person's name.person.ageage of dead, not used, just to keep track of it when that's the only data we have.person.picsis a list of pictures files relative to the yaml file. The first one will be added as person's face. The other pics are not used yet.Richunionswith marriage and divorce annotationsfamily.married: defaults to true. Set it to false to indicate unmarried patnership (⚯). Set it to a date to see⚭YYYY-MM-DDin the union node.family.divorced: defaults to false. Set it to true to indicate a divorce (⚮). Set it to a date to see⚮YYYY-MM-DDin the union node.Attributes forinternal documentation:person.todoa string or list of strings of pending tasks for the personperson.fromorigin of the person (country, city...)person.commentsa string or list of strings of commentsfamily.notesa string or list of strings of notesfamily.docsdocumentation about the family. A list of document paths relative to the yaml file. Not used yet but it is suposed to provide links to the research sources.
Package: nist
nistFrom theNational Institute of Standards and
Technology’sOffice OF Weights and
Measures, one can find a handy list of
all themetric
prefixes:
e.g.PurposeNameSymbolFactorNamelarger quantities or whole unitsquettaQ$$10^{30}$$nonillionronnaR$$10^{27}$$octillionyottaY$$10^{24}$$septillion…………hecto Example: hectareh$$10^{2}$$hundreddeka Example: dekameterda$$10^{1}$$ten$$10^{o}$$onedeci Example: decimeterda$$10^{-1}$$tenthcenti Example: centigramh$$10^{-2}$$hundredth…………yocto Example: yoctosecondy$$10^{-24}$$septillionthrontor$$10^{-27}$$octillionthsmaller quantities or sub unitsquectoq$$10^{-30}$$nonillionthOh how nice it would be to have a class likeunitwhich we could
subclass and use as follows:classsecond(unit):name='second's1=second(1)s1,s1.kilo,s1.milli,s1.to(3),float(s1.to(3))(1.0S,0.001KS,1000.0mS,0.001KS,0.001)ah so easy to convert between and even have clean formatting.InstallpipinstallnistUsagefactWhile eachfact(factor) hasbase: ClassVar[int] = 10,baseis actually an instance
variable.>>>float(kilo()),float(kilo(base=2)),float(kilo(base=5)),float(kilo(base=10))(1000.0,8.0,125.0,1000.0)In case that behavior is not obviousfactis really just a
named and explicilty signed exponent:>>>kb=kilo(base=2)>>>kb.abrv,kb.base,kb.expo,kb.sign,kb.ekey,float(kb)('kilo',2,3,1,3,8.0)Eachfactusesefmtfor its representation by defaultefmtis True, but can be turned off
by settingefmtto False.>>>((decka(),hecto(),kilo(),mega(),giga(),tera()),(decka(showefmt=False),hecto(showefmt=False),kilo(showefmt=False),mega(showefmt=False),giga(showefmt=False),tera(showefmt=False)))((e+1,e+2,e+3,e+6,e+9,e+12),(F1P,F2P,F3P,F6P,F9P,F12P))Actually we have three formats to work with:>>>kilo().fstr,kilo(showbase=True).bstr,kilo(showbase=True).efmt('F3P','10^+3','e+3')unitThe goal of theunitclass is to make it easy to create units:classsecond(unit):name='second'>>>s1=second(1)>>>s1,s1.kilo,s1.milli,s1.to(3),float(s1.to(3)),s1.shownumb(1.0S,0.001KS,1000.0mS,0.001KS,0.001,True)We can also explore all the different ways of viewing formats:importpandasaspdresults=list()factors=(tera,decka,deci,centi,milli,pico)forfclsinfactors:forfltin(1,20,0.03):forfactreprin{'abrv','name','symb'}:forshownumbin(True,False):forabrvunitin(True,False):res=fmtunit(org=flt,flt=flt/float(fcls()),unt=unit,fct=fcls,factrepr=factrepr,shownumb=shownumb,abrvunit=abrvunit,unitname='second',factname=None,ndig=3)results.append(dict(fname=fcls.name,flt=flt,res=res,org=flt/float(fcls()),shownumb=shownumb,abrvunit=abrvunit,factrepr=factrepr))df=pd.DataFrame(results).sort_values(by=['fname','res'])df.head()fnamefltresorgshownumbabrvunitfactrepr134hundredth0.030.03 S3FalseTruename138hundredth0.030.03 S3FalseTrueabrv142hundredth0.030.03 S3FalseTruesymb143hundredth0.030.03 S3FalseFalsesymb135hundredth0.030.03 seconds3FalseFalsename
Package: nista_learn
This is a Python ML librabry like scikit-learnYou can create your ML model or use some ML algorithms on your projectExample: Logistic RegressionRead csv file and slip data into training and test dataimport pandas as pd
df = pd.read_csv('Data_for_UCI_named.csv', header=0)
df['stabf'] = df['stabf'].map({'unstable': 0, 'stable': 1})
Y = df['stabf'].values
# sometimes it's needed to reshape data
X = df.drop(['stabf'], axis=1).values

X_train = X[:9000]
Y_train = Y[:9000]
X_test = X[9000:]
Y_test = Y[9000:]Let’s use our library# call the LogisticRegression class
from nista_learn.regressions import LinearRegression, LogisticRegression

log_reg = LogisticRegression()
# fitting data
log_reg.fit(X_train, Y_train, iterations=200000, learning_rate=0.25, show=True)
# predict a small dataset
y_pred = log_reg.predict(X_test[20:28])
print('--- small value ---')
print(Y_test[20:28])
print('--- predicted data ---')
print(y_pred)
# plotting the cost function
log_reg.plot_cost()
Package: nistats
Nistats is being retired.This repository is now archived.It will not receive any further development or bug fixes.
Its functionality has now been incorporated into Nilearn’sstats,datasets, andreportingmodules.It will be available inNilearn0.7.0 onwards, set to release in late 2020.
Please file issues and pull requests in Nilearn, from now on.Credit for the various Pull Requests that  have been merged into Nistats are now visible in Nilearn.
Open issues have been moved into Nilearn.
Open PRs will need to be redone.NistatsNistats is a Python module for fast and easy modeling and statistical analysis
of functional Magnetic Resonance Imaging data.It leverages thenilearnPython toolbox for
neuroimaging data manipulation (data downloading, visualization, masking).This work is made available by acommunityof
people, amongst which the INRIA Parietal Project Team and D’esposito lab at
Berkeley.It is based on developments initiated in the nipynipyproject.Important linksOfficial source code repo:https://github.com/nistats/nistats/HTML documentation (stable release):http://nistats.github.io/DependenciesThe required dependencies to use the software are:Python >= 2.7setuptoolsNumpy >= 1.11SciPy >= 0.17Nibabel >= 2.0.2Nilearn >= 0.4.0Pandas >= 0.18.0Sklearn >= 0.18.0If you are using nilearn plotting functionalities or running the
examples, matplotlib >= 1.5.1 is required.If you want to run the tests, you need nose >= 1.2.1 and coverage >= 3.7.If you want to download openneuro datasets Boto3 >= 1.2 is requiredInstallThe installation instructions are found on the webpage:https://nistats.github.io/DevelopmentCodeGITYou can check the latest sources with the command:git clone git://github.com/nistats/nistatsor if you have write privileges:git clone git@github.com:nistats/nistats
Package: nistauthenticator
NISTAuthenticatorWith NISTAuthenticator, you can authenticate JupyterHub users using a customized HTTP header.Use CaseThis may come handy if you wish to integrate JupyterHub into a system with an existing authentication system.We can put JupyterHub behind a reverse HTTP proxy and use an authentication middleware that re-directs hub users to the system's login page. Upon successful login, the reverse HTTP proxy adds to the original request a customized HTTP header with value set to the user ID. It then forwards the request to JupyterHub and NISTAuthenticator will authenticate the user via the HTTP header and perform all the necessary actions for subsequent requests to JupyterHub and user's single-user notebook server to work.InstallationYou can install with pip:pipinstallnistauthenticatorUsageAfter successful installation, you can usenistauthenticatoras the authenticator for JupyterHub by including the following in your JupyterHub configuration file:c.JupyterHub.authenticator_class='nistauthenticator.NISTAuthenticator'In the same JupyterHub configuration file, you can configure the name of HTTP header to use for authentication viac.NISTAuthenticator.user_header = 'your-http-header-name'and the logout URL can be specified viac.NISTAuthenticator.logoutURL='your-logout-url'
Package: nistbeacon
Python NIST Randomness BeaconWARNING: DO NOT USE BEACON GENERATED VALUES AS SECRET CRYPTOGRAPHIC
KEYS.InstallationPrerequisitesA required librarypycryptodomeis used withnistbeacon.Ubuntu, and other Linux-based users should havepython3-devinstalled.apt-getinstallpython3-devInstallingnistbeaconTo install the beacon library, simply usepip:pipinstallnistbeaconBeacon UsageIt is easy to use the beacon. Most queries are performed throughNistBeaconwhich producesNistBeaconValueobjects.Beacon Sample CodefromnistbeaconimportNistBeacon# In the examples below I will be using 1447873020# as my <timestamp> when required# Current Record (or next closest)# https://beacon.nist.gov/rest/record/<timestamp>record=NistBeacon.get_record(1447873020)# Previous Record# https://beacon.nist.gov/rest/record/previous/<timestamp>prev_record=NistBeacon.get_previous(1447873020)# Next Record# https://beacon.nist.gov/rest/record/next/<timestamp>next_record=NistBeacon.get_next(1447873020)# First Record# https://beacon.nist.gov/rest/record/1378395540first_record=NistBeacon.get_first_record(download=True)# Last Record# https://beacon.nist.gov/rest/record/lastlast_record=NistBeacon.get_last_record()# Verify the record and the record chainrecord_chain_result=NistBeacon.chain_check(1447873020)Further DocumentationPlease refer to theofficial documentationto dive deeper intoNistBeaconandNistBeaconValueobjects.ContributingPlease refer to theCONTRIBUTINGdocument on GitHubProject HealthBranchBuild StatusCoverage StatusMasterDevelopReferencesNIST Randomness Beacon HomepageNIST Beacon REST API
Package: nist-calculators
NIST calculatorsMy python implementaion of some NIST calculator.Now implemented:NIST XCOM--- photon cross-sections databaseNIST_STAR--- stopping-power & range tables for electrons, protons, and helium ionsInstallationpipinstallnist-calculatorsUsageXCOMSeetutorial.Example of usage:importxcomdata=xcom.calculate_cross_section("He",[1e6,2e6,5e6])# Energy in eV# orimportnumpyasnpenergy=np.array([1e6,2e6,5e6])Z=2data=xcom.calculate_cross_section(Z,energy)ESTARSeetutorial.Example of usage:fromstarimportelectronhydrogen=electron.PredefinedMaterials.HYDROGENdata=electron.calculate_stopping_power(hydrogen,energy=[1e2,2e2,3e2])# Energy in MeVPSTARSeetutorial.Example of usage:fromstarimportProtonSTARCalculator,ProtonMaterialsmaterial=ProtonMaterials.BERYLLIUMcalculator=ProtonSTARCalculator(material)total=calculator.calculate_total_stopping_powers([10,20,50])# Energy in MeVASTARSeetutorial.Example of usage:fromstarimportAlphaSTARCalculator,AlphaMaterialsmaterial=AlphaMaterials.BERYLLIUMcalculator=AlphaSTARCalculator(material)total=calculator.calculate_total_stopping_powers([10,20,50])# Energy in MeV
Package: nistrng
This is apython 3.6and above implementation of theNIST Test Suite for Random Number Generators(RNGs).
The idea behind this work is to make a script oriented object-oriented framework for said tests.
This is born from my research since I required to use the tests inside a python research project and I found existing
implementation to be not well suited to that task without extensive modifications.The NIST reference paper can be found atSP800-22r1a.This work is inspired by the great work of David Johnston (C) 2017, which can be found ongithub.FeaturesAll the test in theNISTpaper vectorized and optimized the best I couldClass structure for each test allowing for easy debug and use, both in script and inside broader applicationsUtility functions to pack the sequence in 8-bits using numpy and to run the tests in multiple waysCache system both at function level and at test level to improve performanceBuilt-in measurement of time required to perform each testDefault Test class and Result class to allow eventual extension to additional testsLicenseBSD 3-Clause LicenseFor additional information check the provided license file.How to installIf you only need to use the framework, just download the pip packagenistrngand import the package in your scripts:pip install nistrngIf you want to improve/modify/extends the framework, or even just try my own simple benchmarks at home, download or clone
the gitrepository. You are welcome to open issues or participate in the project, especially if further optimization is achieved.How to useFor a simple use case, refer to benchmark provided in therepository. For advanced use, refer to the built-in documentation
and to the provided source code in therepository.Current issuesCurrently the slow speed of both the Serial and Approximate Entropy tests is an open issue. Any solution or improvement is
welcome.Can I help?Yes, of course! This project is very side to me, so any help in reporting issues, fixing bugs, testing functionalities and overall improving it is welcome!Changelogv. 1.2.1:Improved safe-guard against eventual NaN values that may arise inside the score calculationsAdded unpack function to return to the original numeric integer value from a 8-bit binary sequenceSome minor fixes and adjustmentsv. 1.2.2:Fixed SP800_22R1A_BATTERY dictionary which was missing the cumulative sums testFixed missing parenthesis on test_approximate_entropy.pyFixed bugs on some tests for very long sequencesSome minor fixes and adjustmentsv. 1.2.3:Fixed binary matrix rank test (credits to @scrambler-crypto)Fixed a frequencies computation bug (w.r.t. tickets) on the linear complexity test