Saravanan666/pypi-org huggingface dataset:
Package: abcpy
ABCpy is a highly modular, scientific library for approximate Bayesian computation (ABC) written in Python. It is designed to run all included ABC algorithms in parallel, either using multiple cores of a single computer or using an Apache Spark or MPI enabled cluster. The modularity helps domain scientists to easily apply ABC to their research without being ABC experts; using ABCpy they can easily run large parallel simulations without much knowledge about parallelization, even without much additional effort to parallelize their code. Further, ABCpy enables ABC experts to easily develop new inference schemes and evaluate them in a standardized environment, and to extend the library with new algorithms. These benefits come mainly from the modularity of ABCpy.
Package: abc-radio-wrapper
ABC Radio WrapperAPI wrapper library for the song history of abc radio channelsFree software: MIT licenseDocumentation:https://abc-radio-wrapper.readthedocs.io.Quick Startimportabc_radio_wrapperABC=abc_radio_wrapper.ABCRadio()search_result=ABC.search(station="triplej")forradio_playinsearch_result.radio_songs:print(radio_play.song.title)forartistinradio_play.song.artists:print(artist.name)FeaturesUse python to search through the radio catalogue of triplej, ABC jazz, doublej and more!full type coverage for fast type hints on modern IDE’s>90% test coverageTODOPull out dataclasses into seperate class filesPull out unittests into seperate filesAdd async queriesCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2023-01-18)First added to githubFirst added to pypi0.1.1 (2023-01-21)Passing build on readthedocs0.1.2 (TBA)Add usage documentationAdd docstrings on all functions (pass pydocstyle audit)0.2.0 (TBA)Include iteration through search resultsInclude async requests0.3.0 (TBA)Add github actions for automatic testing1.0.0 (TBA)
Package: abcrypt-py
Python Bindings for abcryptabcrypt-pyis the Python bindings for theabcryptcrate.UsageInstallationTo install this library:pipinstallabcrypt-pyExamplefromtypingimportFinalimportabcrypt_pyDATA:Final[bytes]=b"Hello, world!\n"PASSPHRASE:Final[bytes]=b"passphrase"# Encrypt `DATA` using `PASSPHRASE`.ciphertext=abcrypt_py.encrypt(DATA,PASSPHRASE)assertciphertext!=DATA# And extract the Argon2 parameters from it.params=abcrypt_py.Params(ciphertext)assertparams.memory_cost==19456assertparams.time_cost==2assertparams.parallelism==1# And decrypt it back.plaintext=abcrypt_py.decrypt(ciphertext,PASSPHRASE)assertplaintext==DATADocumentationSee thedocumentationfor more details.Minimum supported Rust versionThe minimum supported Rust version (MSRV) of this library is v1.74.0.Developmentmaturinis required for development of this library.python3-mvenvvenvsourcevenv/bin/activate
maturindevelop
pip3installabcrypt-py[test,dev]ChangelogPlease seeCHANGELOG.adoc.ContributingPlease seeCONTRIBUTING.adoc.LicenseCopyright © 2022–2024 Shun Sakai (seeAUTHORS.adoc)This library is distributed under the terms of either theApache License 2.0or theMIT License.This project is compliant with version 3.0 of theREUSE Specification. See
copyright notices of individual files for more details on copyright and
licensing information.
Package: abc-sphinx-extensions
ABC-Sphinx-ExtensionA sphinx extension to convert abc files into svg
images for use in a Sphinx document.It usesabcm2ps <http://moinejf.free.fr/>_,  to carry out the conversion.To installFrom sourcegitcogit@github.com:wxtim/abc-sphinx-extensions.git
pipinstall-e.From PyPipipinstallabc-sphinx-extensionsTo useAdd files with the extension.abcin source folder. After building these files will be available
asx.svg(with title rendered) and_x.svg(without title) for filex.abc.
Package: abc-storage
# abs-storage## InstallingBefore work with our storage, you need to install:sudo apt-get install docker.io make docker-composeMake and docker is not required features. This tools is needed for more useful development. We recommend to use Anaconda
or another environment manager for safety system interpreter. You can download Anaconda
[here](https://www.anaconda.com/). After installing Anaconda please create new environment:conda create –name abc-storage python=3.7
conda activate## StartingBefore starting please install all python package dependencies:make depsWe have three mode of starting:full subsystemdevelopmentmake### docker-compose fullWe use docker-compose for local development and starting you service and environment. If you want to start full
subsystem, you need to do this:make run-fullAfter that our service and environment is started. If you want to start our service the first time, docker container
with service is built. Other container is pulled.If you want to start service not first time, maybe you need rebuilt service for apply last changes:make run-rebuild### developmentFor development, you can use only environment:make run-envAfter that, it starts all dependencies services. Now, you can run our service in your IDE for development.### makeFor fast start our service we use command:make run## Environment variablesOur service takes all environments variables from config:deployments/.envs/local.env. More about it you can read into
this file:abc_storage/__service/config.py. You can add new variables there and here:deployments/.envs/local.env.We separates variables by namespaces, therefore we set prefix before variable name. You can see in files, which we
denote above.## TestingWe have three mode of testing:unit testingintegration testingall: unit and integration testingWe have three commands:make test-integrationBefore starting integration tests (above) you need to start environment:make run-envUnit tests:make test-unitAll tests:make test## NoticeWe use makefile as interface for communicate our application with our systems by command line while development and
deployments
Package: abcunit-backend
Storage backends for an ABCUnit Framework, logging success and failures of process units.Database BackendTo use this backend you will need to contact the JASMIN help desk (support@jasmin.ac.uk) and ask them to setup a postgresql database for you. Specify a name for the database and a username to login with. JASMIN support will get back to you with the user password and host name.After you have got your database, you’ll need to export an environment variable called$ABCUNIT_DB_SETTINGSand set it to a connection string forpsycopg2:ABCUNIT_DB_SETTINGS="dbname=<name> user=<user> host=<host> password=<pwd>"DatabaseHandlerclass construction looks like this:DatabaseHandler(table_name="results")Wheretable_nameis the name of the table logs will be insert intoConnects to an existing database and creates a table to store results:<table_name> (id varchar(255) PRIMARY KEY, result varchar(255) NOT NULL)idresultfacet1.facet2.facet3successfacet1.facet2.facet3bad_file&#8942;&#8942;File System BackendFileSystemHandlerclass construction looks like this:FileSytemHandler(base_log_dir, n_facets, sep)Where;base_log_diris the string path to top level directory for logsn_facetsis the number of facets used to describe each unit resultsepis the separator used for a result identifierUses the file system to create log files marking success and failures, categorised by directory structure:<log_base_dir>/success/facet1/facet2/facet3

<log_base_dir>/failure/error_type/facet1/facet2/facet3
Package: abd
abd - Abstract Base Decoratorabd provides anAbstractBaseDecoratorclass which you can inherit from to create flexible decorators.Example>>>fromabdimportABD>>>classDecorator(ABD):...definvoke(self,*args,**kwargs):..."""Must write an invoke function...         invoke is called when the decorated function is called...         """...# catch, edit and pass on the (keyword) arguments...#  that are given the the decorated function...print('invoke is called')...result=self.decorated_object(*args,**kwargs)...# function has been called and result is available...#   possible to edit the result here...returnresult...>>>@Decorator...deffunc(argument):...# some function logic ......returnargument...>>>func('some text')invokeiscalled'some text'>>>PyPIpip install abd
Package: abdal-net-py
Abdal Net PyMade ForPowerful security network package for hackers and all security expertsRequires Libhurry.filesize - psutilRequires pythonPython >= 3.9Featurescalculate internet speed with unitcalculate byte to other unit (show in xbit vs xbyte )Convert byte to other unit by select unitmulti password generatorlog writer + logging ram and cpu usageiranian mobile phone generateHow To installpipinstallabdal-net-pyHow To upgradepipinstallabdal-net-py-UHow To useimportabdal_net_py❤️ DonationUSDT:      TXLasexoQTjKMoWarikkfYRYWWXtbaVadBBTC :   bc1q9w9ymgz2wluax60rsuza4q0at7gpy82g8el6zjETH :   0x402b9f67091Af07224286F16d7377bc50268Db94For Iranian People -> MellatBank : 6104-3378-5301-4247🤵 ProgrammerEbrahim Shafiei (EbraSha)E-Mail =Prof.Shafiei@Gmail.comTelegram:https://t.me/ProfShafieiLicenseAbdal Net Py is open-source software licensed under theMIT license.☠️ Reporting IssuesIf you are facing a configuration issue or something is not working as you expected to be, please use theProf.Shafiei@Gmail.com. Issues on GitLab are also welcomed.⚠️ Legal disclaimer ⚠️Usage of Abdal Net Py for Spying targets without prior mutual consent is illegal. It's the end user's responsibility to obey all applicable local, state and federal laws. Developers assume no liability and are not responsible for any misuse or damage caused by this program.
Package: abd-clam
CLAM: Clustered Learning of Approximate Manifolds (v0.22.3)CLAM is a Rust/Python library for learning approximate manifolds from data.
It is designed to be fast, memory-efficient, easy to use, and scalable for big data applications.CLAM provides utilities for fast search (CAKES) and anomaly detection (CHAODA).As of writing this document, the project is still in a pre-1.0 state.
This means that the API is not yet stable and breaking changes may occur frequently.Installation>python3-mpipinstall"abd_clam==0.22.3"Usagefromabd_clam.searchimportCAKESfromabd_clam.utilsimportsynthetic_data# Get the data.data,_=synthetic_data.bullseye()# data is a numpy.ndarray in this case but it could just as easily be a# numpy.memmap if your data do fit in RAM. We used numpy memmaps for the# research, though they impose file-IO costs.model=CAKES(data,'euclidean')# The CAKES class provides the functionality described in our# [CHESS paper](https://arxiv.org/abs/1908.08551).model.build(max_depth=50)# Build the search tree to depth of 50.# This method can be called again with a higher depth, if needed.query,radius,k=data[0],0.5,10rnn_results=model.rnn_search(query,radius)# This is how we perform ranged nearest neighbors search with radius 0.5 around# the query.knn_results=model.knn_search(query,k)# This is how we perform k-nearest neighbors search for the 10 nearest neighbors# of the query.# The results are returned as a dictionary whose keys are indices into the data# array and whose values are the distance to the query.LicenseMITCitationTODO
Package: ab-decrypt
Decryptor for android backups that were created withadb backup.Installationpython3-mvenv/path/to/venv/path/to/venv/pipinstallab-decryptln-sr/path/to/venv/ab-decrypt~/bin/Usage# Read from stdin, write to stdout$ab-decrypt# Read from stdin, write to stdout$ab-decrypt--# Read from file, write to other file$ab-decryptbackup.abbackup.tar# List backup contents$ab-decryptbackup.ab|tar-tvEnvironment variablesAB_DECRYPT_PASSWORD: Decryption passwordHelp / Bugs / ContributionsPlease file an issue or pull request at GitHub.
Package: abdesign
ABDesignAPIThe ABDesign API provides functions and data structures for dealing with Immunoglobulin sequences. It contains computational methods to 'humanize' an antibody by CDR-grafting.Note:A local installation of ANARCI is needed for this tool. You can downlod ANARCIhere.IgObjectCore data structure. All params are set on initialization, either by using create_annotation(), or manually.seqrequiredchain_typeiso_typespeciesannotationregionsexport_to_json()HumanizationTo 'humanize' a queried sequence, a human template sequence must be provided in form of an IgObject, too. It is also possible to indicate the wanted annotation types for the hybrid IgObject. By default, all annotation systems provided by this package are applied.replace_cdr(ig_query:IgObject, ig_target:IgObject, annotation_types)
Package: abdoTheBest
GeNNGeNN (generative neural networks) is a high-level interface for text applications using PyTorch RNN's.FeaturesPreprocessing:Parsing txt, json, and csv files.NLTK, regex and spacy tokenization support.GloVe and fastText pretrained embeddings, with the ability to fine-tune for your data.Architectures and customization:GPT2 with small, medium, and large variants.LSTM and GRU, with variable size.Variable number of layers and batches.Dropout.Text generation:Random seed sampling from the n first tokens in all instances, or the most frequent token.Top-K sampling for next token prediction with variable K.Nucleus sampling for next token prediction with variable probability threshold.Text Summarization:All GPT2 variants can be trained to perform text summarization.Getting startedHow to installpipinstallgennPrerequisitesPyTorch 1.4.0pipinstalltorch==1.4.0Pytorch Transformerspipinstallpytorch_transformersNumPypipinstallnumpyfastTextpipinstallfasttextUse the package managerpipto install genn.UsageText Generation:RNNs (You can switch LSTMGenerator with GRUGenerator:fromgennimportPreprocessing,LSTMGenerator,GRUGenerator#LSTM exampleds=Preprocessing("data.txt")gen=LSTMGenerator(ds,nLayers=2,batchSize=16,embSize=64,lstmSize=16,epochs=20)#Train the modelgen.run()# Generate 5 new documentsprint(gen.generate_document(5))GPT2 Generator:#GPT2 examplegen=GPT2("data.txt",taskToken="Movie:",epochs=7,variant="medium")#Train the modelgen.run()#Generate 10 new documentsprint(gen.generate_document(10))Text Summarization:GPT2 Summarizer:#GPT2 Summarizer examplefromgennimportGPT2Summarizersumm=GPT2Summarizer("data.txt",epochs=3,batch_size=8)#Train the modelsumm.run()#Create 5 summaries of a source documentsrc_doc="This is the source document to summarize"print(summ.summarize_document(n=5,setSeed=src_doc))For more examples on how to use Preprocessing, please refer tothis file.For more examples on how to use LSTMGenerator and GRUGenerator, please refer tothis file.For more examples on how to use GPT2, please refer tothis file.For more examples on how to use GPT2Summarizer, please refer tothis file.ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.LicenseDistributed under the MIT License. SeeLICENSEfor more information.
Package: abdttsslug
abdttsSum of squareFree software: MIT licenseDocumentation:https://abdttsslug.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.0.1 (2021-04-10)First release on PyPI.0.0.2 (2021-04-10)Minor fixes.0.0.3 (2021-04-10)Minor fixes
Package: abduct
Capture stdout/stderr and optionally release when an exception occurs.fromabductimportcaptured,out,errwithcaptured(out())asstdout:...withcaptured(out(),err())as(stdout,stderr):...@captured(out(),err())...Installation:$pipinstallabductWhen stdout or stderr is captured, the relatedsys.stdoutorsys.stderrobject is replaced with aStringIOobject for the
life of the context.ExamplesIt’s often useful to capture the output of a block of code. Abduct
makes this easy:withcaptured(out())asstdout:print('hello!')assertstdout.getvalue()=='hello!'Sometimes you may want to hide the output of some codeunlesssomething goes wrong. In this case, simply specifyrelease_on_exception=True:withcaptured(out(release_on_exception=True)):print('Really important message!')ifblow_up:raiseRuntimeError()In this case,Really important message!will be printed onstdoutif the exception is raised.If you’d like to capture the output, but still write through tostdoutorstderr, use thetee=Trueparameter:withcaptured(err(tee=True))asstderr:sys.stderr.write('Error!')assertstderr.getvalue()=='Error!'In this case,Error!is capturedandwritten tostderrat the same time.Changelog2.0.1Added tests for the decorator usage.2.0.0Feature: “Create a write-through option for output.”Backwards-incompatible change:stdoutandstderrmethods are nowoutanderrrespectively.1.0.4Fixed Travis release criteria.1.0.3Refactored test runner.1.0.2Fixed README and description.1.0.1Travis config now defers to tox.Added examples to README.1.0.0Actual working code. Yay!0.0.1Initial release.
Package: abdullahfunctions
python-example-packageThis is a starter repo for creating a new python package.TestsSimply run:pytestCode covergeGenerate coverage report with:py.test --cov=myPackage tests/script:- pip install --user --upgrade setuptools wheel twine numpy- python3 setup.py sdist bdist_wheel- python3 -m twine upload --repository-urlhttps://test.pypi.org/legacy/dist/*before_script:- bumpversion minor setup.py
Package: abdur-shopping-cart
Simple Shopping CartThis is a simple shopping cart that allows you to add items into the cart
and display the state of the cart.InstallationTo install the library run the below command in your virtual environmentpip install abdur-shopping-cartCode styleThis project makes use of black with default settings to format the code
and flake 8 as a linter.Usage`
from abdur_shopping_cart.shopping_cart import ShoppingCartcart = ShoppingCart()
print(cart.add_to_cart("cornflakes", 1))
print(cart.add_to_cart("cornflakes", 1))
print(cart.add_to_cart("weetabix", 1))
print(cart.calculate_state())
`APIadd_to_cart(product_name, quantity)Takes in a product name as a string, quantity as an integer
Returns a success/failurecalculate_state()Calculates the current state of the cart
Returns the product names, quantities, sub_total, tax and totalTestingThe project uses pytest to run its testsOtherThe .gitignore file was generated using gitignore.iohttps://www.toptal.com/developers/gitignore/
Package: abeattacks
abeattacksThis python module contain the attacks against ABE schemes presented
at Black Hat Europe 2021 by Antonio de la Piedra and Marloes Venema.At the CT-RSA 2021 conference, Venema and Alpár presented attacks against 11 ABE and MA-ABE schemes, including the highly cited DAC-MACS scheme with applications to the cloud.We demonstrate the practicality of the attacks providing:
- A decryption attack against DAC-MACS, where a single user is able to decrypt ciphertexts with policies she cannot satisfy. This user does not even need to collude with other users or corrupt an authority.
- A decryption attack with corruption of one of the authorities against the YJ14-MA-ABE scheme.
- A decryption attack against the YCT14 scheme where two users collude in order to obtain a decryption key based on the work of Tan et al. and Herranz (2019).RequirementsInstall CHARM using the following commands:add-apt-repository ppa:deadsnakes/ppa -y
apt update -y
apt install python3.7 python3.7-dev -y
apt install python3-virtualenv -y
apt install build-essential sudo python3-dev wget flex bison python3-pip libssl-dev libgmp10 libgmp-dev git openssl -y
rm -f /usr/bin/python && ln -s /usr/bin/python3.7 /usr/bin/python
rm -f /usr/bin/python3 && ln -s /usr/bin/python3.7 /usr/bin/python3
git clone https://github.com/JHUISI/charm
git checkout 55d82436d5da1a830fb16d6536700d9d61c0149d
./configure.sh
python3.7 -m pip install -r requirements.txt
cd charm/deps/pbc
make
ldconfig
cd charm/
make 
make install 
ldconfigReferences- https://www.blackhat.com/eu-21/briefings/schedule/index.html#practical-attacks-against-attribute-based-encryption-25058
- https://eprint.iacr.org/2020/460
Package: abeci
abeciCreateperfect pangrams, sentences with exactly one of each letter in the English alphabet.Generate PangramsAfter installingabecias a package, you can run:abeci-pangramsThis script makes a text file with this name, inresultsby default:./results/std4_max1_has_2x_3x/pangrams_1024_0x1_128x2_512x3_1024x4.txtIf theeffects/source.pfile is missing,The script uses Google's English Corpus of all books published in 2008.The script will wiret aneffects/source.pto speed up future calls to the script.it also:writes a log file such as2022-04-07T0200.log.writes intermediate files in aneffectsdirectory.The file is named after the current date and UTC time.Run the help command for options:abeci-pangrams -hAPI UsageNo programatic api is documented at this time:Look tosrc/modules/savePangrams.pyfor inspirationLocal InstallationDependenciesInstallMinicondaor Anaconda.conda env create -f environment.yamlorconda env update -f environment.yamlThen, activate the installed environment:conda activate abeciAfter installing dependencies, you can run:python src/pangrams.pyRun the help command for options:python src/pangrams.py -hBuild Locally w/ pipUpgrade pip and build with pippython -m pip install --upgrade pip
pip install buildInstall LocallyUsing the conda environment, install locally with pip:VIRTUAL_ENV=$CONDA_PREFIX pip install --src $VIRTUAL_ENV/src -e .TestAfter installing locally, run:bash run_test.shPublishPublishing happens on release. The following two links were inspirations:Publish to pipPublish to conda
Package: abeec
abeec 🐝 --- an ABC samplerabeecis a sampler to perform Approximate Bayesian Computation (ABC) --- i.e., likelihood free posterior inference! It is based on the algorithm presented inIshida et al. (2015).Author: Nestor Espinoza (nespinoza@stsci.edu)Statement of needWhile for Cosmological applications an ABC sampler has already been published by the team of Ishida et al (cosmoabc---check their repository!),
there was a need to develop a more general scheme to allow some flexibility to the sampler. For instance, doing arbitrary prior distributions (e.g., with priors that might be correlated)
was not straightforward to implement, as well as have external functions for distances and simulators that could all benefit from a common parallelization scheme. On top of that, I wanted
a simple sampler that used the most basicpythonlibraries (e.g.,numpyandscipy) at its core. That's whereabeeccomes into place.Using the libraryTo perform ABC on a given dataset, you need three ingredients:Apriorfrom which to draw points.Adistanceto compute distances from simulated datasets to your dataset.And asimulator, to simulate datasets to compare against your dataset.Inabeec, it is expected the user will provideclassesdefining theprior, thedistanceand thesimulator. All the sampler does it take those and apply the iterative importance
sampling scheme outlined inIshida et al. (2015), giving back a sample from the posterior. Once those classes are written, one might simply run the
sampler as:import abeec
    from your_script import prior, distance, simulator

    samples = abeec.sample(prior, distance, simulator)The best is to check the examples underexamples.InstallationInstallation is as simple as:python setup.py installOr via PyPi:pip install abeecLicence and attributionRead theLICENCEfile for licencing details on how to use the code. If you make use of this code, please citeIshida et al. (2015)and link back
to this repository.