Saravanan666/pypi-org huggingface dataset:
Package: oq
otQuick access to code favorites.We all have our go-to utils and awesome tools.
Some of us are annoyed at having to type long dotpaths to the object we want to import.
Some of us probably forget where that particular object is in the first place.This is to mitigate that.Instead do this:fromot.my_medleyimportthat_objectAlso have severalmy_medleysets of favorites, since what you'll need depends on the context.Word of advice: This tool is meant for the quick-and-dirty development contextDon't use this for production or any long-term code. It's meant for pulling things together quickly. Once the code matures, you should import "normally".UsageSee what modules are available to import>>>fromotimportsource_module_names>>>source_module_names['tw','ca']See what that module is about (if the author cared to say)>>>fromotimporttw>>>print(tw.__doc__)Amedleyofmymostfrequentlyusedtools.Import it (as a module object)fromotimporttw# or from ot import tw as my_own_nameInject the module's contents in your current namespace (if you're that type)fromot.twimport*Inject everything there is in your namespace (but expect unpredictable name collisions)fromotimport*
Package: oqi
OpenQasmInterpreter (oqi)This package interprets a .qasm 2.0 file and then outputs the results to the console. All of the executions are made using the qasm simulator. Moreover you can specify different options to run your circuit.To just run your .qasm file you'll have to run:oqi filenameAdditionally if you want to see and run your circuit you can execute:oqi filename dFinally if you want to run your circuitnnumber of times you can specify the repetitions:oqi filename d noroqi filename nIn any case the output will include something of the form:The results are: {'bits': numberOfMeasurements, ...}InstallationInstall it with the pip package manager:pip install oqiFeaturesCustom errors:Draw and run your circuits:AcknowledgementsThe token analyser and many other things needed in order to run the interpreter came from theqiskit repo. My contribution was to create the pip package and some wrapping in order to run their interpreter.
Package: oqmscore
OQMscores-pythonObjective Quality Measure scores for speech samples in python.Subjective listening tests provide perhaps the most reliable method for assessment of speech quality. These tests, however, can be time consuming requiring in most cases access to trained listeners. For these reasons, several researchers have investigated the possibility of devising objective, rather than subjective, measures of speech quality. Ideally, the objective measure should be able to assess the quality of the processed speech without needing access to the original speech signal. For futher understanding refer tolink.The OQM scores implemented in this package are:PESQ (Perceptual Evaluation of Speech Quality)SegSNR (Segmental Signal to Noise ratio)LLR (Log likelihood ratio)WSS (Weighted spectral slope)Requirements:LibrosaScipyNumpyPESQ python wrapperInstallation:pip install oqmscoreReferences:The PESQ implementation is done usingPESQ python wrapperThe SegSNR, WSS and LLR implementations are take fromSEGAN-repositoryto-dos:Write unit testsWrite doc strings for the classes and functions properly.
Package: oqo
📦 setup.py (for humans)This repo exists to providean example setup.pyfile, that can be used
to bootstrap your next Python project. It includes some advanced
patterns and best practices forsetup.py, as well as some
commented–out nice–to–haves.For example, thissetup.pyprovides a$ python setup.py uploadcommand, which creates auniversal wheel(andsdist) and uploads
your package toPyPiusingTwine, without the need for an annoyingsetup.cfgfile. It also creates/uploads a new git tag, automatically.In short,setup.pyfiles can be daunting to approach, when first
starting out — even Guido has been heard saying, "everyone cargo cults
thems". It's true — so, I want this repo to be the best place to
copy–paste from :)Check out the example!Installationcdyour_project# Download the setup.py file:#  download with wgetwgethttps://raw.githubusercontent.com/navdeep-G/setup.py/master/setup.py-Osetup.py#  download with curlcurl-Ohttps://raw.githubusercontent.com/navdeep-G/setup.py/master/setup.pyTo DoTests via$ setup.py test(if it's concise).Pull requests are encouraged!More ResourcesWhat is setup.py?on Stack OverflowOfficial Python Packaging User GuideThe Hitchhiker's Guide to PackagingCookiecutter template for a Python packageLicenseThis is free and unencumbered software released into the public domain.Anyone is free to copy, modify, publish, use, compile, sell, or
distribute this software, either in source code form or as a compiled
binary, for any purpose, commercial or non-commercial, and by any means.
Package: oqtant
Oqtant API (OqtAPI)Oqtant offers a Python package that works with Jupyter notebooks to provide access to more complex experiments, fine-tuned control over job inputs, and more in-depth data analysis tooling.WithOqtant Quantum Matter Service, create a form of quantum matter called a Bose-Einstein condensate (BEC), wherein quantum phenomena are visible on a macroscopic scale. Using light, programmatically manipulate BECs to explore a multitude of phenomena: superposition, interference, tunneling, superfluidity, and more!🚀 InstallationThe easiest way to install Oqtant's Python API is via pip:pipinstalloqtantUpdateFrom a terminal, run:pipinstall--upgradeoqtantOr from within a Jupyter notebook, run:%pipinstall--upgradeoqtantTo submit jobs, you will need an Oqtant account.Register and start for free!For more details, see theQuick Start Guide🧭 IntroductionAfter installing Oqtant's Python API, you can explore theExample Jupyter Notebooks. These notebooks will walk you through the functionality and capabilities of using the Oqtant API. Additionally, there is a growing library ofDemonstration Jupyter Notebooksfor showing specific quantum phenomena on Oqtant (e.g. interference).Oqtant's Python API contains tools to:Have the programmatic equivalent of the same job functionality as theOqtant Web App, and moreBuild parameterized (i.e. optimization) experiments using OqtantJobsSubmit and retrieve OqtantJob resultsAnalyze OqtantJob results📓 DocumentationQuick Start GuideOqtant API docsOqtant REST API docsExample Notebooks
Package: oqupy
NOTE:The former packageTimeEvolvingMPOis now integrated intoOQuPy.OQuPy: Open Quantum Systems in PythonA Python 3 package to efficiently compute non-Markovian open quantum systems.This open source project aims to facilitate versatile numerical tools to efficiently compute the dynamics of quantum systems that are possibly strongly coupled to structured environments. It allows to conveniently apply several numerical methods related to the time evolving matrix product operator (TEMPO) [1-2] and the process tensor (PT) approach to open quantum
systems [3-5]. This includes methods to compute ...the dynamics of a quantum system strongly coupled to a bosonic environment [1-2].the process tensor of a quantum system strongly coupled to a bosonic environment [3-4].optimal control procedures for non-Markovian open quantum systems [5].the dynamics of a strongly coupled bosonic environment [6].the dynamics of a quantum system coupled to multiple non-Markovian environments [7].the dynamics of a chain of non-Markovian open quantum systems [8].the dynamics of an open many-body system with one-to-all light-matter coupling [9].Up to versions 0.1.x this package was calledTimeEvolvingMPO.[1]Strathearn et al.,New J. Phys. 19(9), p.093009(2017).[2]Strathearn et al.,Nat. Commun. 9, 3322(2018).[3]Pollock et al.,Phys. Rev. A 97, 012127(2018).[4]Jørgensen and Pollock,Phys. Rev. Lett. 123, 240602(2019).[5]Fux et al.,Phys. Rev. Lett. 126, 200401(2021).[6]Gribben et al.,arXiv:20106.0412(2021).[7]Gribben et al.,PRX Quantum 3, 10321(2022).[8]Fux et al.,arXiv:2201.05529(2022).[9]Fowler-Wright at al.,Phys. Rev. Lett. 129, 173001(2022).LinksGithub:https://github.com/tempoCollaboration/OQupyDocumentation:https://oqupy.readthedocs.ioPyPI:https://pypi.org/project/oqupy/Tutorial:InstallationYou can install OQuPy using pip like this:$ python3 -m pip install oqupySee thedocumentationfor more information.Quickstart TutorialClick thelaunch binderbutton above to start a tutorial in a browser based jupyter notebook (no installation required) or checkout thetutorial in the documentation.ContributingContributions of all kinds are welcome! Get in touch if you ...... found a bug.... have a question on how to use the code.... have a suggestion, on how to improve the code or documentation.... would like to get involved in writing code or documentation.... have some other thoughts or suggestions.Please, feel free to file an issue in theIssuessection on GitHub for this. Also, have a look atCONTRIBUTING.mdif you want to get involved in the development.Citing, Authors and BibliographySee the filesHOW_TO_CITE.md,AUTHORS.mdandBIBLIOGRAPHY.md.
Package: ora2
Open Response AssessmentThis repository defines an Open Response Assessment (ORA) XBlock for use within edx-platform:https://github.com/openedx/edx-platformUser docsInstallation, Tests, and other Developer TasksIf you’ll be modifying ORA code, theOpen edX Developer Stackis a Docker-based development environment.edX engineers follow theguides on our wiki.
Reading this page before contributingis highly recommended.LicenseThe code in this repository is licensed under version 3 of the AGPL unless
otherwise noted.Please seeLICENSE.txtfor details.How to ContributeContributions are very welcome. The easiest way is to fork this repo, and then make a pull request from your fork. The first time you make a pull request, you may be asked to sign a Contributor Agreement.Reporting Security IssuesPlease do not report security issues in public. Please emailsecurity@openedx.orgMailing List and SlackYou can get help with this code on ourmailing listsor in real-time conversations onSlack.
Package: orabote-biz
orabote.bizScrubberFree software: MIT licenseDocumentation:https://orabote-biz.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2019-07-23)First release on PyPI.
Package: oracle2postgres
Oracle to PostgresThe oracle2postgres package can be used to migrate data from Oracle to Postgres. It uses SQLAlchemy as an intermediary to map data types between the two database systems. The approach is hacky, but it worked for us!Example NotebookFor example usage, see the Jupyter Notebook at:https://github.com/MIT-LCP/oracle-to-postgres/blob/master/migration.ipynb.Instructions for usepip install oracle2postgresFollow the instructions in the Jupyter Notebook at:https://github.com/MIT-LCP/oracle-to-postgres/blob/master/migration.ipynb.
Package: oracle-automlx
Automated Machine Learning with Explainability (AutoMLx)The AutoMLx package provides advanced automated machine learning solutions and machine learning model explanations for tabular and text datasets.TheAutoML Pipelineautomatically preprocesses, selects and engineers high-quality features in your dataset, which are then given to an automatically chosen and tuned machine learning model.TheMLExplaineroffers a wide variety of visual and interactive explanations. For example, these include (local and global) feature importance, feature dependence and counterfactual explanations. These explanations provide multi-facetted insights into what your (AutoMLx or scikit-learn-style) model has learned and whether or not you should trust it.Thefairness moduleoffers tools to help you diagnose and understand the unintended bias present in your dataset and model so that you can make steps towards more inclusive and fair applications of machine learning.InstallationThere are two ways to use AutoMLx.Direct InstallationAutoMLx can be installed on x86 or ARM machines in a python 3.8 or 3.10 environment using:pip3 install oracle-automlxSeveral AutoMLx dependencies are optional and can be installed with:pip3 install oracle-automlx[option]where "option" can be one of:"classic", which installs the libraries needed to support AutoML for tabular classification,
regression and anomaly detection."viz", which provides visualization functionality for explanations and the AutoML time-series forecaster,"forecasting", which installs the forecasting models needed for the AutoML time-series forecaster,"deep-learning", which installs some torch-based deep-learning models for the AutoML classifier, regressor and anomaly detector."onnx", which installs the onnx-related libraries needed to export AutoML models to the ONNX format."explain",which installs the libraries needed to use the MLExplainer to compute machine.Multiple optional dependencies can be installed simultaneously using a comma-separated list. For example:pip3 install oracle-automlx[forecasting,viz]Oracle Cloud Infrastructure (OCI) Data Science (DS) Conda PackAutoMLx is also available in theOracle Cloud InfrastructureData Scienceservice in theAutoMLxconda pack.
Package: oraclecloud
Python library to consume REST API of Oracle Public Cloud.Currently the library is tested with Python 2.7, 3.5 and 3.6.* UNOFFICIAL Python Library - Not maintained by Oracle *Installing### Install with pipUsepipto install from PyPI.Python 2:`sh pip install oraclecloud `Python 3:`sh pip3 install oraclecloud `UsageSee example of usage in the examples folder.AuthorWritten by Waslley Souza.MIT license, all text above must be included in any redistribution
Package: oracle-connection
This package is a new tool to connect Oracle database.One important thing is thatI’m not the first authorof this package.
At the early time, this package was write by my ex-leader in 2017.
And it wasn’t a package, just a python file namedmy_modules.
Then, we modified it last year. At last she left this company, Then
I refactored the code, deleted some methods uselsee.ExampleTo connect:fromoracle_connectionimportConnectOraclefromoracle_connectionimportDatabaseOperateconn=ConnectOracle("your host","your instance name","your username","your password")cursor=DatabaseOperate(conn)# operationscursor.close_cursor()Get datas from database:# ...sql="""SELECT field1, field2 FROM table"""# get all rowsrows=cursor.get_all(sql)# get first rowrow=cursor.get_one(sql)# get all datas and reformat it to a dataframedf=cursor.get_df(sql)sql="""SELECT field FROM table"""# get one column and reformat it to a listrow_list=cursor.one_column_list(sql)cursor.close_cursor()The other operations:# ...value1,value2,value3=1,2.2,"c"sql="""INSERT INTO table (field1, field2, field3
         ) VALUES (:value1, :value2, :value3)"""# insert one row into tablecursor.execute_sql(sql,value1=value1,value2=value2,value3=value3)sql="""INSERT INTO table (field1, field2, field3
         ) VALUES (:1, :2, :3)"""data=[[1,2.2,"c"],[2,2.1,"d"],[3,4.2,"f"],[4,6.2,"g"]]# insert many rows into tablecursor.insert_data(sql,data=data)sql="""DELETE FROM table WHERE field=:value"""# delete datas from tablecursor.execute_sql(sql,value=value1)
Package: oracledao
Oracle-DAO project=======================Módulo para criar uma camada de conexao a banco de dados Oracle, com padrão Singletone prover interface para métodos basicos de DML, Select e chamada a Procedures/FunçõessPre-requisito: cx_OracleCompatível com Python 3.----Changelog0.1.1 - Incluído nos parâmetros de conexão o enconding UTF-8#todo: include a module's overview and basic usage examples.
Package: oracledb
python-oracledbpython-oracledb is aPython programming languageextension module
allowing Python programs to connect toOracle Database.  It is the
renamed, new major release of the popular cx_Oracle driver.The module conforms to thePython Database API 2.0 specificationwith
a considerable number of additions and a couple of minor exclusions, see thefeature list.Synchronous andconcurrentcoding styles are supported.InstallationRunpython -m pip install oracledbSeepython-oracledb Installation.Dependencies and InteroperabilityPython versions 3.7 through 3.12.Prebuilt packages are available for these Python versions on Windows, on
macOS and on Linux.Source code is also available.Oracle Client libraries areoptional.Thin mode: By default python-oracledb runs in a 'Thin' mode which
connects directly to Oracle Database.Thick mode: Some advanced Oracle Database functionality is currently only
available when optional Oracle Client libraries are loaded by
python-oracledb.  Libraries are available in the freeOracle Instant
Clientpackages. Python-oracledb can use Oracle Client
libraries 11.2 through 21c.Oracle DatabaseThin mode: Oracle Database 12.1 (or later) is required.Thick mode: Oracle Database 11.2 (or later) is required, depending on the
Oracle Client library version.  Oracle Database's standard client-server
version interoperability allows connection to both older and newer
databases. For example when python-oracledb uses Oracle Client 19c libraries,
then it can connect to Oracle Database 11.2 or later.DocumentationSee thepython-oracledb DocumentationandRelease
Notes.SamplesExamples can be found in the/samplesdirectory and thePython and Oracle Database Tutorial.HelpQuestions can be asked inGithub Discussions.Problem reports can be raised inGitHub Issues.TestsSee/testsContributingThis project welcomes contributions from the community. Before submitting a
pull request, pleasereview our contribution guide.SecurityPlease consult thesecurity guidefor our responsible security
vulnerability disclosure process.LicenseSeeLICENSE,THIRD_PARTY_LICENSES, andNOTICE.
Package: oracle-eth
Copyright (c) 2019 yuyongpengPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Description: UNKNOWN
Keywords: solidity web3 constract sdk
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Topic :: Utilities
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Software Development :: Libraries
Requires-Python: >=3.6
Package: oracle-guardian-ai
Oracle Guardian AI Open Source ProjectOracle Guardian AI Open Source Project is a library consisting of tools to assess fairness/bias and privacy of machine learning models and data sets. This package containsfairnessandprivacy_estimationmodules.TheFairnessmodule offers tools to help you diagnose and understand the unintended bias present in your dataset and model so that you can make steps towards more inclusive and fair applications of machine learning.ThePrivacy Estimationmodule helps estimate potential leakage of sensitive information in the training data through attacks on Machine Learning (ML) models. The main idea is to carry out Membership Inference Attacks on a given target model trained on a given sensitive dataset, and measure their success to estimate the risk of leakage.InstallationYou have various options when installingoracle-guardian-ai.Installing the oracle-guardian-ai base packagepython3-mpipinstalloracle-guardian-aiInstalling extras librariesTheall-optionalmodule will install all optional dependencies. Note the single quotes around installation of extra libraries.python3-mpipinstall'oracle-guardian-ai[all-optional]'To work with fairness/bias, install thefairnessmodule. You can find extra dependencies inrequirements-fairness.txt.python3-mpipinstall'oracle-guardian-ai[fairness]'To work with privacy estimation, install theprivacymodule. You can find extra dependencies inrequirements-privacy.txt.python3-mpipinstall'oracle-guardian-ai[privacy]'DocumentationOracle Guardian AI DocumentationOCI Data Science and AI services ExamplesOracle AI & Data Science BlogExamplesMeasurement with a Fairness Metricfromguardian_ai.fairness.metricsimportModelStatisticalParityScorerfairness_score=ModelStatisticalParityScorer(protected_attributes='<target_attribute>')Bias Mitigationfromguardian_ai.fairness.bias_mitigationimportModelBiasMitigatorbias_mitigated_model=ModelBiasMitigator(model,protected_attribute_names='<target_attribute>',fairness_metric="statistical_parity",accuracy_metric="balanced_accuracy",)bias_mitigated_model.fit(X_val,y_val)bias_mitigated_model.predict(X_test)ContributingThis project welcomes contributions from the community. Before submitting a pull request, please review ourcontribution guide.Find Getting Started instructions for developers inREADME-development.md.SecurityConsult the security guideSECURITY.mdfor our responsible security vulnerability disclosure process.LicenseCopyright (c) 2023 Oracle and/or its affiliates. Licensed under theUniversal Permissive License v1.0.
Package: oracle-json-field
# Oracle Json Fielddjango_json_field is a reusable Django field that allows you to store validated JSON your database and your modelIt is heavily inspired by the Postgres Json Field and this generic json field:https://raw.githubusercontent.com/dmkoch/django-jsonfield/## PreRequisitesTo use oracle-json-field you will need Django 2.0+ and Python3+## InstallationInstall via pippip install oracle-json-fieldOr install via sourcepython setup.py install## SetupUpdate Django settings as followsINSTALLED_APPS += ('oracle_json_field',)## Define your modelsfrom oracle_json_field.fields import JSONFieldfrom oracle_json_field.managers import JsonQueryManagerclass JsonModel(models.Model):json = JSONField()default_json = JSONField(default={"key1": 50})complex_default_json = JSONField(default=[{"key1": 50}])empty_default = JSONField(default={})objects = JsonQueryManager()## Create and **Natively** query any field in your jsonJsonModel.objects.create(json={"person": {"age": 25,"first_name": "Joe","last_name": "Blogs","address": {"number": "52","street": "Here Terrace","post_code": "ABC 123","city": "Anytown",}}})JsonModel.objects.filter_json(json__person__first_name='Joe')JsonModel.objects.filter_json(json__person__first_name__startswith='J')JsonModel.objects.filter_json(json__person__address__city__in=['Anytown', 'Sometown'])JsonModel.objects.filter_json(json__person__age__gte=25)## Running the test suite:In order to run the test suite, you will need to create an oracle userand export the following environment variables:ORACLE_JSON_HOSTORACLE_JSON_PORTORACLE_JSON_SIDORACLE_JSON_USERORACLE_JSON_PASSThis link describes how to setup the testing user:https://code.djangoproject.com/wiki/OracleTestSetupThen run the following commandpython setup.py testYou should then see:running testCreating test database for alias 'default'...Creating test user...System check identified no issues (0 silenced).......................................Preserving test database for alias 'default'...----------------------------------------------------------------------Ran 38 tests in 4.900sOK
Package: oracle-kernel
oracle_kernelOracle Kernel for Jupyter中文介绍Installationstep1:pip(3) install oracle_kernelTo get the newest one from this repo (note that we are in the alpha stage, so there may be frequent updates), type:pip(3) install git+git://github.com/Hourout/oracle_kernel.gitstep2:Add kernel to your jupyter:python(3) -m oracle_kernel.installALL DONE! 🎉🎉🎉Uninstallstep1:View and remove oracle kerneljupyter kernelspec list
jupyter kernelspec remove oraclestep2:uninstall oracle kernel:pip(3) uninstall oracle-kernelALL DONE! 🎉🎉🎉Usingjupyter notebookstep1: you should set oracle host and portstep2: write your oracle codeQuotekernel logohttps://sensorstechforum.com/oracle-fixed-270-security-flaws-products/