Saravanan666/pypi-org huggingface dataset:
Package: abel
Vectorfromabel.linalg.vectorimportVectora,b=Vector([1,2]),Vector([3,4])c,d=Vector([1,2,3]),Vector([4,5,6])asserta.shape==b.shape==(1,2)assertc.shape==d.shape==(1,3)Additionasserta+a==Vector([2,4])asserta+b==Vector([4,6])assertb+b==Vector([6,8])Subtractionasserta-b==Vector([-2,-2])assertb-a==Vector([2,2])Scalingasserta*5==Vector([5,10])assert5*a==Vector([5,10])asserta/2==Vector([0.5,1.0])Dot (inner) productasserta@b==11asserta@a==5Norm (length)asserta.norm()-2.236<0.001assertb.norm()-5<0.001AngleassertVector.angle(a,a)<0.001assertVector.angle(a,b)-0.1799<0.001assertVector.angle(a,b)==Vector.angle(b,a)Vector projectionassertVector.proj(a,a)==aassertVector.proj(a,b)==Vector([1.32,1.76])assertVector.proj(b,a)==Vector([2.2,4.4])Scalar projectionassertVector.scalproj(a,b)-4.919<0.01assertVector.scalproj(b,a)-2.2<0.01assertVector.scalproj(a,a)-2.236<0.01assertVector.scalproj(b,b)-5<0.1Cross productassertVector.cross(c,d)==Vector([-3,6,-3])Average (arithmetic mean)assertVector.average(Vector([2,1]),Vector([4,2]))==Vector([3.0,1.5])CollinearityVectors are collinear iff one is a scalar multiple of the other.assertVector.collinear(Vector([2,1]),Vector([4,2]))assertVector.collinear(Vector([-3,4,1]),Vector([-15,20,5]))assertnotVector.collinear(Vector([0,1]),Vector([1,0]))Linear independenceA set of vectors is linearly independent iff all vectors in it are pairwise non-collinear.assertVector.linindep(Vector([0,1]),Vector([1,0]))assertVector.linindep(Vector([1,1]),Vector([2,1]))assertnotVector.linindep(Vector([1,2,3]),Vector([0,0,1]),Vector([0,0,2]))
Package: abelian
abelianis a Python library for computations on elementary locally compact abelian groups (LCAs).
The elementary LCAs are the groups R, Z, T = R/Z, Z_n and direct sums of these.
The Fourier transformation is defined on these groups.
Withabelianit is possible to sample, periodize and perform Fourier
analysis on elementary LCAs using homomorphisms between groups.Classes and methodsThe most important classes are listed below. The software contains many other functions and methods not listed.TheLCAclass represents elementary LCAs, i.e. R, Z, T = R/Z, Z_n and direct sums of these groups.Fundamental methods: identity LCA, direct sums, equality, isomorphic, element projection, Pontryagin dual.TheHomLCAclass represents homomorphisms between LCAs.Fundamental methods: identity morphism, zero morphism, equality, composition, evaluation, stacking, element-wise operations, kernel,    cokernel, image, coimage, dual (adjoint) morphism.TheLCAFuncclass represents functions from LCAs to complex numbers.Fundamental methods: evaluation, composition, shift (translation), pullback, pushforward, point-wise operators (i.e. addition).ExampleThe following example shows Fourier analysis on a hexagonal lattice.We create a Gaussian on R^2 and a homomorphism for sampling.fromabelianimportLCA,HomLCA,LCAFunc,voronoifrommathimportexp,pi,sqrtZ=LCA(orders=[0],discrete=[True])R=LCA(orders=[0],discrete=[False])# Create the Gaussian function on R^2function=LCAFunc(lambdax:exp(-pi*sum(j**2forjinx)),domain=R**2)# Create an hexagonal sampling homomorphism (lattice on R^2)phi=HomLCA([[1,1/2],[0,sqrt(3)/2]],source=Z**2,target=R**2)phi=phi*(1/7)# Downcale the hexagonfunction_sampled=function.pullback(phi)Next we approximate the two-dimensional integral of the Gaussian.# Approximate the two dimensional integral of the Gaussianscaling_factor=phi.A.det()integral_sum=0forelementinphi.source.elements_by_maxnorm(list(range(20))):integral_sum+=function_sampled(element)print(integral_sum*scaling_factor)# 0.999999997457763We use the FFT to move approximate the Fourier transform of the Gaussian.# Sample, periodize and take DFT of the Gaussianphi_p=HomLCA([[10,0],[0,10]],source=Z**2,target=Z**2)periodized=function_sampled.pushforward(phi_p.cokernel())dual_func=periodized.dft()# Interpret the output of the DFT on R^2phi_periodize_ann=phi_p.annihilator()# Compute a Voronoi transversal function, interpret on R^2sigma=voronoi(phi.dual(),norm_p=2)factor=phi_p.A.det()*scaling_factortotal_error=0forelementindual_func.domain.elements_by_maxnorm():value=dual_func(element)coords_on_R=sigma(phi_periodize_ann(element))# The Gaussian is invariant under Fourier transformation, so we can# compare the error using the analytical expressiontrue_val=function(coords_on_R)approximated_val=abs(value)total_error+=abs(true_val-approximated_val*factor)asserttotal_error<10e-15Please seethe documentationfor more examples and information.
Package: abelmokadem-awsapilib
A python library that exposes AWS services that are not covered by boto3, through the usage of undocumented APIs.Documentation:https://awsapilib.readthedocs.org/en/latestDevelopment WorkflowThe workflow supports the following stepslinttestbuilddocumentuploadgraphThese actions are supported out of the box by the corresponding scripts under _CI/scripts directory with sane defaults based on best practices.
Sourcing setup_aliases.ps1 for windows powershell or setup_aliases.sh in bash on Mac or Linux will provide with handy aliases for the shell of all those commands prepended with an underscore.The bootstrap script creates a .venv directory inside the project directory hosting the virtual environment. It uses pipenv for that.
It is called by all other scripts before they do anything. So one could simple start by calling _lint and that would set up everything before it tried to actually lint the projectOnce the code is ready to be delivered the _tag script should be called accepting one of three arguments, patch, minor, major following the semantic versioning scheme.
So for the initial delivery one would call$ _tag –minorwhich would bump the version of the project to 0.1.0 tag it in git and do a push and also ask for the change and automagically update HISTORY.rst with the version and the change provided.So the full workflow after git is initialized is:repeat as necessary (of course it could be test - code - lint :) )codelinttestcommit and pushdevelop more through the code-lint-test cycletag (with the appropriate argument)buildupload (if you want to host your package in pypi)document (of course this could be run at any point)Important InformationThis template is based on pipenv. In order to be compatible with requirements.txt so the actual created package can be used by any part of the existing python ecosystem some hacks were needed.
So when building a package out of thisdo notsimple call$ python setup.py sdist bdist_eggas this will produce an unusable artifact with files missing.Instead use the provided build and upload scripts that create all the necessary files in the artifact.Project FeaturesPlease look into the usage files.History0.0.1 (26-04-2021)First code creation0.1.0 (11-05-2021)Initial release0.1.1 (17-05-2021)Filtering out failed accounts from checking their update status0.1.2 (17-05-2021)Fixed a timing issue with getting the active service catalog product on account creation.0.2.0 (18-05-2021)Exposed governed and non governed regions and a small fix with latest update changes.0.2.1 (18-05-2021)Dynamically retrieving updatable information about control tower.0.2.2 (19-05-2021)Added some blocking on actions to prevent race conditions.
Package: abel-pytorch
How to decay your Learning Rate (PyTorch)PyTorch implementation ofABELLRScheduler based on weight-norm. If you find this work interesting, do consider starring the repository. If you use this in your research, don't forget to cite!Original paperDocsInstallationWIP - not available on PyPi yet.pip install abel-pytorchUsageimporttorchfromtorchimportnn,optimfromabelimportABELmodel=resnet18()optim=optim.SGD(model.parameters(),1e-3)scheduler=ABEL(optim,0.9)fori,(images,labels)inenumerate(trainloader):# forward pass...optim.step()scheduler.step()Cite original paper:@article{lewkowycz2021decay,
  title={How to decay your learning rate},
  author={Lewkowycz, Aitor},
  journal={arXiv preprint arXiv:2103.12682},
  year={2021}
}Cite this work:@misc{abel2021pytorch,
  author = {Vaibhav Balloli},
  title = {A PyTorch implementation of ABEL},
  year = {2021},
  howpublished = {\url{https://github.com/tourdeml/abel-pytorch}}
}
Package: abeluna
AbelunaA simple GUI to-do/task manager with CalDAV support. In theory, Abeluna should support any CalDAV server, but currently onlyNextcloudandRadicaleare tested.The goal of this application is to become a desktop version of Nextcloud's Tasks app. As such, not all functionality in theicalendar's VTODOare supported, only those that are used by Nextcloud. On the other hand, there some non-standard fields used by Nextcloud that are supported by Abeluna, such as the ability to hide subtasks.ScreenshotsInstallationFrom PyPIFirst, install two packages,libnotifyandgobject-introspection. On other distributions besides Arch Linux, these names may be different. For example, on Debian-based systems,gobject-introspectionislibgirepository1.0-dev.$pipinstallabeluna
$abelunaAURIf you are using Arch Linux and do not wish to install through PIP, you can install the AUR packageabelunafor the latest version.Usage$abelunaIn the GUI, calendars can be added throughSettings > Calendar settings. General settings, such as the timezone and synchronization schedule can be accessed throughSettings > General settings.Future PlansSupport for desktop notifications.Support for recurring tasks.Add common keyboard shortcuts.
Package: abem
Acoustic Boundary Element Method (abem)AcousticBEM is a small library and example programs for solving the acoustic
Helmholtz equation using the Boundary Element Method. The library is a
re-implementation of the core components of Stephen Kirkup's ABEM Fortran
library and its example programs.
The original Fortran code and the book "The Boundary Element Method in
Acoustics" are available on his website:http://www.boundary-element-method.com/.
Package: abero
runnerAnalyze multiple files for similarity and/or uniqueness.
Finding similarities of works duplicated from one or more part of different works to create a seemingly unique one can be difficult because of different strategies being used, but this can be done better with a software like runner.RequirementsArkivistpip install arkivistUsageInstall the latest abero package, upcoming versions might introduce unannounced changes, so a virtual environment is a must have before installation.pipinstall-UaberoTo integrate abero into your Python codes, check the code snippet below:importaberoabero.analyze(directory,extension="txt",threshold=80,template=None,skipnames=0,group=0,unzip=0,reset=0)CLI Usage# usage: runner [-h] -d directory [-e extension] [-c control] [-t threshold] [-u unzip] [-s skipnames] [-g group] [-r reset]pyrunner.py-d"<path_to_files>"-e"txt"-c"<path_to_control_file>"-t1-u1-s1-r1-d <path>- Full path of the dirctory containing the files to analyze.-e <txt>- List of allowed file extensions to analyze.-c <*.txt>- Full path of the control file.-t <80>- Threshold level for uniqueness, treats similarity below threshold as unique (1-100; default = 0)-u <0>- Unzip/extract ZIP files (0-1; default = 0)-s <0>- Skip files with common names (0-1; default = 0)-g <1>- Only compare if files contains the same identifier (0-1; default = 1)Example:student1*_set1*.py >> student2*_set1*.py-r <0>- Reset analytics before execution (0-1; default = 0)Control FileControl file contains words or phrases, checked line-by-line, that are deem allowed to be contained in all files to analyzed; therefore, if found on the test files, it will not be flagged as duplicate work.FeaturesUnzip featureFile comparisonThreshold levelsSkip / group compareDiff tool, content viewerDid you know?The repository nameaberowas inspired from the words aberrant and runner (Latin), which may mean deviating or being absent.
Package: abexp
ABexpABexpis aPythonlibrary which aims to support users along the entire end-to-end A/B test experiment flow
(see picture below). It contains A/B testing modules which use both frequentist and bayesian statistical approaches
including bayesian generalized linear model (GLM).InstallationThis library is distributed onPyPIand
can be installed withpip. The latest release is version0.0.1.$ pip install abexpThe command above will automatically install all the dependencies listed inrequirements.txt. Please visit theinstallationpage for more details.Getting startedA short example, illustrating it use:import abexpCompute the minimum sample size needed for an A/B test experiment with two variants, so called control and treatment
groups.from abexp.core.design import SampleSize

c = 0.33  # conversion rate control group
t = 0.31  # conversion rate treatment group

sample_size = SampleSize.ssd_prop(prop_contr=c, prop_treat=t)  # minimum sample size per each groupDocumentationFor more information please read the fulldocumentationandtutorials.Info for developersThe source code of the project is available onGitHub.$ git clone https://github.com/PlaytikaResearch/abexp.gitYou can install the library and the dependencies with one of the following commands:$ pip install .                        # install library + dependencies
$ pip install .[develop]               # install library + dependencies + developer-dependencies
$ pip install -r requirements.txt      # install dependencies
$ pip install -r requirements-dev.txt  # install developer-dependenciesAs suggested by the authors ofpymc3andpandoc, we highly recommend to install these dependencies withconda:$ conda install -c conda-forge pandoc
$ conda install -c conda-forge pymc3To create the fileabexp.whlfor the installation withpiprun the following command:$ python setup.py sdist bdist_wheelTo create the HTML documentation run the following commands:$ cd docs
$ make htmlRun testsTests can be executed withpytestrunning the following commands:$ cd tests
$ pytest                                      # run all tests
$ pytest test_testmodule.py                   # run all tests within a module
$ pytest test_testmodule.py -k test_testname  # run only 1 testLicenseMIT License
Package: abf-explorer
ABF ExplorerABF_Exploreris asimplegraphical application for quickly viewing axon binary format (ABF) files from electrophysiology experiments without writing a bunch of boilerplate python/matlab/etc. This graphical tool allows you to quickly scroll through files, read the basic metadata, and take a look at the data.We use the excellent pyABF package (https://github.com/swharden/pyABF) byScott W Hardento do the hard work of parsing ABF files.UseThis is a graphical application for inspecting physiology data in ABF files. ABF Explorer supports Python > 3.7.Installpipinstallabf_explorerLaunch GUIabf_explorerCommand line options-dor--startup-dirspecify path to a directory containing ABF files.Use:abf_explorer-dpath/to/abfsKeyboard shorcutsTABwill plot the current selectioncwill clear the current plotSample dataSample data (abf's) can be downloaded fromthis repository.Changelogabf_explorer==0.6Date: 2021-04-21Reason: Bump PyQt5 version to fix BigSur GUI failure.
Package: abg-python
abg_pythonSome Python utilities common to a lot of my packages and repositories (perhaps most notablyFIREstudioand, in part,Firefly).If you use any part of this code for direct calculation please put a nice thank you in your acknowledgements ("Some of the calculations presented in this work rely on public code developed by Alex Gurvich, available at github.com/agurvich/abg_python" or something like that). If you use it as a dependency for something else I've built, acknowledge that codebase instead (e.g. FIRE studio or Firefly).Installationabg_pythonis available from PyPi bypipinstallabg_pythonor you can build the source yourself by cloning this repository and, from the top level directory, executingpipinstall-e.
Package: abhakliste
AbhaklisteAbhakliste is a minimal task runner that prints a list of tasks and their status.
It runs a collection of dependent task which can be shell commands or python functions in sequence
and checks for error. Even if one task fails, it continues to run the rest of the tasks.
The goal of this project is to provide a minimal task runner with a low overhead API.⚙️ InstallationInstall the project withpippipinstallabhakliste🎨 FeaturesLow Overhead Task RunnerVisual summary of task resultsWritten in pure python (no modules)Supports Python 3.8+💡 Usage ExamplesimportsubprocessfromabhaklisteimportAbhakliste# set up runnerabhaker=Abhakliste()# run code contextwithabhaker.run_context(desc="Run ls"):subprocess.check_output("ls")# run cli commandabhaker.run_cmd("ls",desc="Run ls")# run functiondefrun_ls():subprocess.check_output("ls")abhaker.run_func(run_ls,desc="Run ls")# raise an error if a run failedabhaker.raise_on_error()📜 DocumentationFor further examples on how to use the modules and a detailed API reference, see thedocumentation.
Package: abhiprime
Summary:This is an efficient prime number tester program created to check if the given number is a prime number or not.The file is availabe onhttps://pypi.org/project/abhiprime/#filesSteps to use:Open the code editor like jupyter or google colab where you can install packages. Like in Jupyter type-!pip install abhiprimeOne can also use command prompt. Open the path where python is installed and type-pip install abhiprimeOnce installation is done open your IDE and type-import abhiprimeorimport abhprime as apPress enterNow type- abhiprime.test_prime(any number which you want to check)If you are using alias name for abhiprime then type- ap.test_prime(any number which you want to check)If you get ModuleNotFoundError (It may happen if you install via command prompt), then make sure that path specified for python is correct and is added in environment path variable.If you are already using abhiprime then please upgrade it, using$ pip install --upgrade abhiprimeIf upgradation is throwing error, then uninstall the previous version and install the latest version.After upgradation, typeimport abhiprime as ap
from abhiprime import *Now you can use it to check the following operations:ap.test_prime(n)- This is an efficient prime number tester program created to check if the given number is a prime number or not.
ap.prev_prime(n)- used to give the just previous prime number value from the given number n.
ap.next_prime(n)- used to give the just next prime number value from the given number n. 
ap.prime_upto(n)- gives a list of prime numbers upto the given number n.
ap.range_prime(n,m)- gives a list of prime numbers between numbers n and m.
ap.fib_prime(n)- gives a list of numbers which are prime and fibonacci also.
ap.prime_factor(n)- gives a list of numbers which are prime prime factors of the given number.Change Log6.0.0(15/07/2021)Eighth Release
Package: abhishek-helloworld
Hello WorldThis is an example project demonstrating how to publish a python module to PyPI.InstallationRun the following to install:pipinstallhelloworldUsagefromhelloworldimportsay_hello# Generate "Hello, World!"say_hello()# Generate "Hello, Everybody!"say_hello("Everybody")Developing Hello WorldTo install helloworld, along with the tools you need to develop and run tests, run the following in your virutalenv:$pipinstall-e.[dev]$pipinstall-e.'[dev]'# for ZSH$pipinstall-e."[dev]"# for Windows
Package: abhkumar10-helloworld
Hello WorldThis is an example project demonstrating how to publish a python module to PyPI.InstallationRun the following to install:pipinstallabhkumar10-helloworldUsagefromhelloworldimportsay_hello# Generate "Hello, World!"say_hello()# Generate "Hello, Everybody!"say_hello("Everybody")Developing Hello WorldTo install helloworld, along with the tools you need to develop and run tests, run the following in your virutalenv:$pipinstall-e.[dev]$pipinstall-e.'[dev]'# for ZSH$pipinstall-e."[dev]"# for Windows
Package: abi2solc
abi2solcA library for generating Solidity interfaces from ABIs.InstallationYou can install the latest release viapip:$pipinstallabi2solcOr clone the repo and usesetuptoolsfor the most up-to-date version:$pythonsetup.pyinstallUsage>>>importabi2solc>>>abi=[{'constant':False,'inputs':[{'name':'spender','type':'address'},...>>>interface=abi2solc.generate_interface(abi,"TestInterface")>>>print(interface)'''pragma solidity ^0.5.0;interface ExampleInterface {event Approval (address indexed tokenOwner, address indexed spender, uint256 tokens);event Transfer (address indexed from, address indexed to, uint256 tokens);function approve (address spender, uint256 tokens) external returns (bool success);function transfer (address to, uint256 tokens) external returns (bool success);function transferFrom (address from, address to, uint256 tokens) external returns (bool success);function allowance (address tokenOwner, address spender) external view returns (uint256 remaining);function balanceOf (address tokenOwner) external view returns (uint256 balance);function totalSupply () external view returns (uint256);}'''Supported VersionsBy default,abi2solcgenerates interfaces with pragma^0.5.0With thesolc4=Truekwarg, interfaces are generated with pragma^0.4.17Ifsolc4=Trueand the ABI also contains tuple types, anabstract base contractis generated with pragma^0.4.22TestsTo run the test suite:$toxTests make use ofpy-solc-x.DevelopmentThis project is still under active development and should be considered a beta. Comments, questions, criticisms and pull requests are welcomed! Feel free to open an issue if you encounter a problem or would like to suggest a new feature.LicenseThis project is licensed under theMIT license.
Package: abi3info
abi3infoabi3info exposes information about CPython's "limited API" (including the
stable ABI, calledabi3) as a Python library.Installationabi3info is available viapip:$pipinstallabi3infoUsageabi3info exposes limited API and stable ABI information in the form of a set
of top-level dictionaries, namely:importabi3infoabi3info.FEATURE_MACROSabi3info.MACROSabi3info.STRUCTSabi3info.TYPEDEFSabi3info.FUNCTIONSabi3info.DATASEach of these is a mapping of a name (either asstrorSymbol) to
a data model describing the kind of item (e.g.FeatureMacroorFunction).See the generated documentationfor
more details, including comprehensive type hints and explanations of each data
model.See also thestable_abi.tomlfile, taken from
the CPython sources, which describes each model and their semantics.ExamplesGet information about a particular function:fromabi3infoimportFUNCTIONSfromabi3info.modelsimportSymbolfunc=FUNCTIONS[Symbol("_Py_NegativeRefcount")]print(func.symbol,func.added,func.ifdef,func.abi_only)Get information about the feature macros that control the limited API:fromabi3infoimportFEATURE_MACROSprint(fmforfminFEATURE_MACROS.values())Licensingabi3info is licensed under the MIT license.abi3info is partially generated from metadata retrieved from theCPython sources,
which is licensed under thePSF license.