Saravanan666/pypi-org huggingface dataset:
Package: openscap-report
OpenSCAP Report GeneratorTool for generating report from results of oscap scan.InstallationLearn how to install tool in the documentation.Example usageThis command consumes the ARF file, which is one of the possible standardized formats for the results of SCAP-compliant scanners. You can read about generating ARF report files using OpenSCAP in the OpenSCAP User Manual. Or you can use test arf files from repository/tests/test_data.oscap-report<ssg-fedora-ds-arf.xml>report.htmlMore information about command line usage inman page(man oscap-report) or onreadthedocs
Package: openschc
What is OpenSCHC ?OpenSCHCOpenSCHC is an open-source implementation of SCHC (Static Context Header Compression) standardized at the IETF by theLPWAN Working Group <https://tools.ietf.org/wg/lpwan/>_ asRFC8724 <https://www.rfc-editor.org/info/rfc8724>_.
Oversimplifying, this is essentially IP protocol header compression and fragmentation so that they can be transported by low datarate, long range IoT networks.The long-term goal is to have a stable, open-source, reference Python3 codebase for the SCHC protocol... OpenSCHC is developed to be compatible with MicroPython, on the device side.OpenSCHC is licensed under theMIT License <https://github.com/openschc/openschc/blob/master/LICENSE>_.Project DocumentationThe full documentation about OpenSCHC is availablehere <https://openschc.github.io/openschc>_ .run from sourcepip install -r requirements.txtpython -m openschcbuildpip install --upgrade pip setuptools buildpython -m buildinstall locallypip install .After installation, verify you can theopenschccommmand from any directory.uninstall locallypip uninstall openschcAfter installation, verify you can no longer run theopenschccommmand.upload to PyPIcreate an API token after logging in athttps://pypi.org/(it's a long string starting withpypi-)pip install --upgrade twinetwine upload dist/*username:__token__password: the entire token above, including thepypi-prefixupdate appears athttps://pypi.org/project/openschc/
Package: openschemas
# OpenSchemas Python[![CircleCI](https://circleci.com/gh/openschemas/openschemas-python.svg?style=svg)](https://circleci.com/gh/openschemas/openschemas-python)[![PyPI version](https://badge.fury.io/py/openschemas.svg)](https://badge.fury.io/py/openschemas)![https://github.com/openschemas/spec-template/raw/master/img/hexagon_square_small.png](https://github.com/openschemas/spec-template/raw/master/img/hexagon_square_small.png)-------------------------------------------------------------------------------This is a small module with helper functions and scripts for interacting with schema.orgspecifications, provided as an [openschemas](https://www.github.com/openschemas) tool.Please contribute at [openschemas/openschemas-python](https://www.github.com/openschemas/openschemas-python) onGithub. Documentation will be provided as the library is developed.# Quick StartTo install```git clone https://www.github.com/openschemas/openschemas-pythoncd openschemas-pythonpython setup.py install```or the package is hosted on [pypi](https://pypi.org/project/openschemas/) andcan be installed with pip:```$ pip install openschemas# See the version$ openschemas --version# Run map2model$ map2model --config configuration.yml --folder specifications# Run specification validator$ openschemas validate --criteria openschemas/main/validate/criteria/dummy.yml --infile Container.html```For usage, please see our [documentation](https://openschemas.github.io/openschemas-python/html/usage.html) **under development**
Package: openscm-calibration
Repository healthCalibration tools for simple climate models (and other things perhaps,
surprise us with your applications).Full documentation can be found athttps://openscm-calibration.readthedocs.io/.InstallationOpenSCM-Calibration can be installed with conda or pip:pipinstallopenscm-calibrationcondainstall-cconda-forgeopenscm-calibrationAdditional dependencies can be installed using# To add plotting dependenciespipinstallopenscm-calibration[plots]# To run the notebookspipinstallopenscm-calibration[notebooks,plots]# If you are installing with conda, we recommend
# installing the extras by hand because there is no stable
# solution yet (issue here: https://github.com/conda/conda/issues/7502)For developersFor development, we rely onpoetryfor all our
dependency management. To get started, you will need to make sure that poetry
is installed
(https://python-poetry.org/docs/#installing-with-the-official-installer, we
found that pipx and pip worked better to install on a Mac).For all of work, we use ourMakefile.
You can read the instructions out and run the commands by hand if you wish,
but we generally discourage this because it can be error prone and doesn’t
update if dependencies change (e.g. the environment is updated).
In order to create your environment, runmakevirtual-environment-B.If there are any issues, the messages from theMakefileshould guide you
through. If not, please raise an issue in theissue tracker.
Package: openscm-runner
OpenSCM-RunnerOpenSCM-Runner provides a unified API for running emissions scenarios with different simple climate models.PyPI :Other info :Full documentation can be found at:openscm-runner.readthedocs.io.
We recommend reading the docs there because the internal documentation links
don't render correctly on GitHub's viewer.InstallationOpenSCM-Runner can be installed with conda or pip:pipinstallopenscm-runner
condainstall-cconda-forgeopenscm-runnerAdditional dependencies can be installed using# To add notebook dependenciespipinstallopenscm-runner[notebooks]# To add dependencies for all modelspipinstallopenscm-runner[models]# To add dependencies for MAGICCpipinstallopenscm-runner[magicc]# To add dependencies for FaIRpipinstallopenscm-runner[fair]# CICERO-SCM's Fortran binary requires no additional dependencies to be# installed# To add dependencies for CICERO-SCM's Python portpipinstallopenscm-runner[ciceroscmpy]# If you are installing with conda, we recommend# installing the extras by hand because there is no stable# solution yet (issue here: https://github.com/conda/conda/issues/7502)For developersFor development, we rely onpoetryfor all our
dependency management. To get started, you will need to make sure that poetry
is installed
(instructions here,
we found that pipx and pip worked better to install on a Mac).For all of work, we use ourMakefile.
You can read the instructions out and run the commands by hand if you wish,
but we generally discourage this because it can be error prone.
In order to create your environment, runmake virtual-environment.If there are any issues, the messages from theMakefileshould guide you
through. If not, please raise an issue in theissue tracker.For the rest of our developer docs, please see.
Package: openscm-twolayermodel
OpenSCM two layer model contains implementations of the two layer radiative forcing driven models byHeld et al.,Geoffroy et al.andBloch-Johnson et al.OpenSCM Two Layer Model was designed to provide a clean, modularised, extensible interface for one of the most commonly used simple climate models.
It was used inPhase 1 of the Reduced Complexity Model Intercomparison Projectas a point of comparison for the other participating models.TheFaIR modelimplements a mathematically equivalent model (under certain assumptions) but does not provide as clear a conversion between the two-layer model and the two-timescale response as is provided here.
We hope that this implementation could interface with other simple climate models like FaIR to allow simpler exploration of the combined behaviour of interacting climate components with minimal coupling headaches.As implemented here, the “OpenSCM Two Layer Model” interface is target at researchers and as an education tool.
Users from other fields are of course encouraged to use it if they find it helpful.LicenseOpenSCM two layer model is free software under a BSD 3-Clause License, seeLICENSE.
Package: openscm-units
OpenSCM-UnitsHandling of units related to simple climate modelling.PyPI :Other info :Full documentation can be found at:openscm-units.readthedocs.io.
We recommend reading the docs there because the internal documentation links
don't render correctly on GitHub's viewer.InstallationOpenSCM-Units can be installed with conda or pip:pipinstallopenscm-units
condainstall-cconda-forgeopenscm-unitsFor developersFor development, we rely onpoetryfor all our
dependency management. To get started, you will need to make sure that poetry
is installed
(instructions here,
we found that pipx and pip worked better to install on a Mac).For all of work, we use ourMakefile.
You can read the instructions out and run the commands by hand if you wish,
but we generally discourage this because it can be error prone.
In order to create your environment, runmake virtual-environment.If there are any issues, the messages from theMakefileshould guide you
through. If not, please raise an issue in theissue tracker.For the rest of our developer docs, please see.
Package: openscreen
Openscreen Python SDKOpenscreenis a developer-first platform that enables innovative customer engagement and commerce solutions using QR Codes.
Additional information on using the Openscreen python SDK can be found in the developerdocumentation.Requirementspython3.10.0 or aboveYou will need your Openscreen account key and secret to get started. If you do not have an Openscreen account, you can sign-up for freehere.Installationpip3 install openscreenGetting StartedOpenscreen authenticates your API requests using your account's API key and API secret key. You will also need the ID of the project you wish to work with. Your API key, secret key and project ID can all be found in your by logging into your Openscreendashboard. The following examples demonstrates how to use these values in the SDK to generate your first QR code.fromopenscreenimportOpenscreen,save_qr_image_data_to_file#Obtain your access_key and secret by logging into your Openscreen dashboardos=Openscreen(access_key='YOUR_ACCESS_KEY',access_secret='YOUR_SECRET_KEY')#You must create a project and obtain project_id from your Openscreen dashboardmy_asset=os.project('YOUR_PROJECT_ID').assets().create({'name':'My First QR Code','description':'Dynamic QR Code','qr_codes':[{'intent':'https://www.openscreen.com','intent_type':'DYNAMIC_REDIRECT','dynamic_redirect_type':'NO_SCAN_ID','locator_key_type':'SHORT_URL','status':'ACTIVE'}]})qr_code=my_asset.asset.qr_codes[0]save_qr_image_data_to_file(qr_code.image_data,'qr_code.png')LinksOpenscreen WebsiteDeveloper DocumentationSolutions and Use-cases
Package: opensdk
Devcontainers Contrib CLIInstallationpip install devcontainer-contrib[generate]Feature Generation:Usage: python -m devcontainer_contrib features generate [OPTIONS]
                                                        FEATURE_DEFINITION
                                                        OUTPUT_DIR

Arguments:
  FEATURE_DEFINITION  [required]
  OUTPUT_DIR          [required]

Options:
  --help                          Show this message and exit.Input for feature generation is afeature-definition.jsonfile
This is anextendedversion of thedevcontainer-feature.jsonfile withadditional fields:{...regular devcontainer-json fields  like id,name,description etc...# dependencies are list of features and options  (those will be installed as prerequisites to your feature)"dependencies":[{"feature":"ghcr.io/devcontainers-contrib/features/asdf-package:latest","options":{"plugin":"act","version":"latest"}}],# This command will be executed after the dependency feature list has been installed"install_command":"echo'Done'",# this example test scenario checks the default options (empty options dict), each test_command should exit wth code `0` if your feature is installed correctly."test_scenarios":[{"name":"test_defaults","image":"mcr.microsoft.com/devcontainers/base:debian","test_commands":["act--version"],"options":{}}]}redirect input{..."options":{"version":{"type":"string","default":"latest","description":"Selecttheversionofacttoinstall."}},# you can also redirect one of your actual devcontainer-feature.json options values into a dependency input, note the `$options.version` pointer to the asdf-package version option"dependencies":[{"feature":"ghcr.io/devcontainers-contrib/features/asdf-package:latest","options":{"plugin":"act","version":"$options.version"}}],...}Usage examplethis will generate the Elixir feature
It assumes you have the devcontainer-contrib cli already installedgitclonehttps://github.com/devcontainers-contrib/cli--depth1cdcli


devcontainer-contribfeaturesgenerate"./test/resources/test_feature_definitions/elixir-asdf/feature-definition.json""./output_dir"
Package: openSDUC
# openSDUCTheOpen Stochastic Daily Unit Commitment of Thermal and ESS Units(openSDUC)determines the system operation for supplying the demand at minimum cost.TheopenSDUCmodel presents a decision support system for defining the generation operation of a large-scale electric system.The scope of the model corresponds to one day or one week divided in load levels: 01-01 00:00:00+01:00 to 01-21 23:00:00+01:00.
The time division allows a flexible representation of the periods for evaluating the system operation. For example, by 84 periods of two hours or by the 168 hours of the week.It considers stochastic short-term yearly uncertainties (scenarios) related to the system operation. The operation scenarios are associated with renewable energy sources and electricity demand.The model formulates an optimization problem including binary and continuous operation decisions.Theunit commitment (UC)model is a based on atight and compactformulation including operating reserves. It considers differentenergy storage systems (ESS), e.g., pumped-storage hydro,
battery, etc.The main results of the model can be structured in these topics:Operation: the output of different units and technologies (thermal, storage hydro, pumped-storage hydro, RES), RES curtailmentEmissions: CO2Marginal: Short-Run Marginal Costs (SRMC)A careful implementation has been done to avoid numerical problems by scaling parameters, variables and equations of the optimization problem allowing the model to be used for large-scale cases.
Package: opensea
Opensea APIAn API wrapper library for opensea api.Installationpip3installopenseaUsageRetrieving assets:fromopenseaimportget_assets# This will return a list of assets which you can iterate and get the needed dataasset_list=get_assets(limit=10,verified_only=False)asset=asset_list[0]# Get the first asset obejct from the listprint(asset.name)print(asset.description)print(asset.asset_url)print(asset.get_floor_price())# Floor price of the collectionRetrieving bundles:fromopenseaimportget_bundles# This will return a list of assets which you can iterate and get the needed databundles_list=get_bundles(limit=10)bundle=bundles_list[0]# Get the first asset obejct from the listprint(bundle.slug)print(bundle.assets[0].name)Retrieving stats from specific collection:fromopenseaimportget_collection_stats# This will return a CollectionStats Objectstats=get_collection_stats(collection="doodles-official")print(stats.count)print(stats.num_owners)print(stats.floor_price)
Package: opensearch-benchmark
OpenSearch BenchmarkOpenSearch Benchmark is the macrobenchmarking framework for OpenSearch.What is OpenSearch Benchmark?If you are looking to performance test OpenSearch, then OpenSearch Benchmark is for you. It can help you with the following tasks:Running performance benchmarks and recording resultsSetting up and tearing down OpenSearch clusters for benchmarkingManaging benchmark data and specifications across OpenSearch versionsDiscovering performance problems by attaching so-called telemetry devicesComparing performance resultsCreating customized workloadsWe have also put considerable effort into OpenSearch Benchmark to ensure that benchmarking data are reproducible.DocumentationOfficial documentation for OpenSearch Benchmark isavailable online.Quick StartWant to get started with OpenSearch Benchmark quickly? SeeOpenSearch Benchmark's Quick Start guide in the documentation.Getting helpQuick help:opensearch-benchmark --helpWant to contribute? Look atOpenSearch Benchmark's Developer Guidefor more informationFor any questions or answers, visit ourcommunity forum.File improvements or bug reports in ourGithub repo.How to ContributeSee all details in thecontributor guidelines.LicenseThis software is licensed under the Apache License, version 2 ("ALv2"), quoted below.Copyright 2015-2022 OpenSearchhttps://opensearch.org/Licensed under the Apache License, Version 2.0 (the "License"); you may not
use this file except in compliance with the License. You may obtain a copy of
the License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
License for the specific language governing permissions and limitations under
the License.
Package: opensearch-dsl
WarningThe OpenSearch high-level Python client (opensearch-dsl-py) will be deprecated after version 2.1.0. We recommend switching to the OpenSearch Python client (opensearch-py), which now includes the functionality of opensearch-dsl-py.OpenSearch DSL Python ClientWelcome!Getting StartedCompatibility with OpenSearchProject ResourcesCode of ConductLicenseCopyrightWelcome!opensearch-dsl-pyisa community-driven, open source forkof elasticsearch-dsl-py licensed under theApache v2.0 License. It is a Python client specifically used for build and running queries against OpenSearch built on top ofopensearch-py. For more information, seeopensearch.org.Getting StartedTo get started with the OpenSearch DSL Python Client, seeUSER GUIDE.Compatibility with OpenSearchSeeCOMPATIBILITY.Project ResourcesProject WebsiteDownloads.DocumentationNeed help? TryForumsProject PrinciplesContributing to OpenSearchMaintainer ResponsibilitiesRelease ManagementAdmin ResponsibilitiesSecurityCode of ConductThis project has adopted theAmazon Open Source Code of Conduct. For more information see theCode of Conduct FAQ, or contactopensource-codeofconduct@amazon.comwith any additional questions or comments.LicenseThis project is licensed under theApache v2.0 License.CopyrightCopyright OpenSearch Contributors. SeeNOTICEfor details.
Package: opensearch-haystack
OpenSearch Document StoreDocument Store for Haystack 2.x, supports OpenSearch.Installationpip install opensearch-haystackTestingTo run tests first start a Docker container running OpenSearch. We provide a utilitydocker-compose.ymlfor that:docker-compose upThen run tests:hatch run testLicenseopensearch-haystackis distributed under the terms of theApache-2.0license.
Package: opensearch-helper
Open Search Helper (opensearch-helper)A collection of methods for assisting with OpenSearch querying on AWSMultiMatch Query GeneratorMethod Definitionmultimatch_generator(input_text:str,size:int=5,*args)->MultiMatchQueryInvoke FunctionPass in one-or-more field names after the query:fromopensearch_helperimportmultimatch_generatord_query=multimatch_generator("what is the average PH of rainwater?""question","context")Sample Output{"size":5,"query":{"multi_match":{"query":"input_text","fields":["question"]}}}API Query (AWS)Method Definitionquery(d_query:MultiMatchQuery,index_name:str)->OpenSearchResultInvoke FunctionThe following environment variables must exist and be encrypted viabaseblock::Run-EncryptOPENSEARCH_HOSTOPENSEARCH_REGIONOPENSEARCH_USERNAMEOPENSEARCH_PASSWORDfromopensearch_helperimportqueryquery(d_query,index_name='myindex')Local OpenSearchFrom the terminal rundocker-composeupThe following environment variables must exist and be encrypted viabaseblock::Run-EncryptOPENSEARCH_HOSTOPENSEARCH_USERNAMEOPENSEARCH_PASSWORDUnless these have been modified, the default values can be found herehttps://opensearch.org/docs/latest/opensearch/install/docker/from a python script importfromopensearch_helperimportOpenSearchDEVThe following functions are availableclient=OpenSearchDEV()client.create_index(...)client.delete_index(...)client.add(...)client.query(...)
Package: opensearch-log
opensearch-logopensearch-log is a Python logging handler for direct and efficient log transmission to
OpenSearch or AWS CloudWatch.It can also be used for local JSON logging to stdout.With context manager or function decorator you can add additional fields to log messages.Documentationopensearch-logDevelopersDo not forget to run. ./activate.sh.Scriptsmake helpCoverage reportCodecovCoveralls
Package: opensearch-orm
OpenSearch ORMopensearch-ormis a high-level OpenSearch ORM for Python. The query syntax is similar to django-orm.May be compatible with Elasticsearch, depending on opensearch-py.Installationpipinstallopensearch-ormGetting StartedFirst, define your document model with indexing pattern.fromopensearchormimportSearchSession,BaseModelclassUserLog(BaseModel):__index__='user_access_log-*'method:strpath:strremote_ip:strcreated:datetimeYou can use django-like syntax or typed query expressions together.filter# {'bool': {'must_not': [], 'should': [], 'filter': [{'range': {'created': {'gte': '2022-09-01'}}}, {'match_phrase': {'remote_ip': '127.0.0.1'}}]}}withSearchSession()assession:result=(session.select(UserLog).filter(created__gte='2022-09-01',remote_ip='127.0.0.1').fetch())print(result)# equals toresult=(session.select(UserLog).filter(Range('created',date(2022,9,1)),remote_ip='127.0.0.1').fetch())contains# {'bool': {'must_not': [], 'should': [], 'filter': [{'bool': {'should': [{'match_phrase': {'method': 'GET'}}, {'match_phrase': {'method': 'POST'}}], 'minimum_should_match': 1}}]}}withSearchSession()assession:result=(session.select(UserLog).filter(method__contains=['GET','POST']).fetch())print(result)# equals toresult=(session.select(UserLog).filter(Contains('method',['GET','POST'])).fetch())# single valueresult=(session.select(UserLog).filter(method__contains='GET').fetch())print(result)exclude# {'bool': {'must_not': [{'match_phrase': {'method': 'get'}}, {'match_phrase': {'path': '/login'}}], 'should': [], 'filter': []}}withSearchSession()assession:result=(session.select(UserLog).exclude(method='get',path='/login').fetch())print(result)paginatewithSearchSession()assession:result=(session.select(UserLog).filter(method='get').limit(100).offset(100).fetch())print(result)aggregationsgroup by path and count unique remote_ip.withSearchSession()assession:# aggregate text field need use a keyword field instead# request_timeout argument will be passed on to the opensearch-pyresult=(session.select(UserLog).aggregate(Terms('path.keyword').nested(Cardinality('remote_ip,keyword')),request_timeout=300))print(result)# result -> {'path': 1, 'path2': 2}scrollwith SearchSession() as session:
    start = datetime(2023, 4, 15, 19, tzinfo=TZ)
    end = datetime(2023, 4, 15, 21, tzinfo=TZ)

    scroll = (
        session.select(Model)
        .filter(
            created__gte=start,
            created__lte=end,
        )
        .limit(10000)
        .scroll('1m')
    )
    for records in scroll:
        print('-------scroll', len(records))
Package: opensearch-py
OpenSearch Python ClientWelcome!User GuideAPI DocCompatibility with OpenSearchProject ResourcesCode of ConductLicenseCopyrightWelcome!opensearch-pyisa community-driven, open source forkof elasticsearch-py licensed under theApache v2.0 License.
For more information, seeopensearch.organd theAPI Doc.User GuideTo get started with the OpenSearch Python Client, seeUser Guide. This repository also containsworking samplesandbenchmarks.Compatibility with OpenSearchSeeCompatibility.Project ResourcesProject WebsiteDownloadsDocumentationNeed help? TryForumsProject PrinciplesContributing to OpenSearchMaintainer ResponsibilitiesRelease ManagementAdmin ResponsibilitiesSecurityCode of ConductThis project has adopted theAmazon Open Source Code of Conduct.
For more information see theCode of Conduct FAQ, or contactopensource-codeofconduct@amazon.comwith any additional questions or comments.LicenseThis project is licensed under theApache v2.0 License.CopyrightCopyright OpenSearch Contributors. SeeNOTICEfor details.