Saravanan666/pypi-org huggingface dataset:
Package: oarepo-sitemaps
OARepo sitemapsInstalationpipinstalloarepo-sitemapsUsageOARepo module that creates sitemap for a repository. Sitemap includes only records, that are in state "published" and can be viewed by public.
Created sitemap is accessible onserver/sitemap.xmlurl.ConfigurationDefault number of record per one sitemap is 10000, this can be changed in configuration with'SITEMAP_MAX_URL_COUNTset to a different number.Example<?xml version="1.0" encoding="utf-8"?><urlsetxmlns="http://www.sitemaps.org/schemas/sitemap/0.9"xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"xsi:schemaLocation="http://www.sitemaps.org/schemas/sitemap/0.9 http://www.sitemaps.org/schemas/sitemap/0.9/sitemap.xsd"><url><loc>http://localhost:5000/cesnet/records/1</loc><lastmod>2021-05-24</lastmod><changefreq>weakly</changefreq><priority>1</priority></url><url><loc>http://localhost:5000/cesnet/records/2</loc><lastmod>2021-05-24</lastmod><changefreq>weakly</changefreq><priority>1</priority></url></urlset>ChangesVersion 1.0.0Initial public release.
Package: oarepo-tokens
oarepo-tokensOARepo library generating tokens for upload from command-line utilityoarepo-s3-cliInstallationTBDConfigurationTBDAPIPOST /access-tokens/statusReturns status of token sent in auth.header.POST /access-tokens/revokeRevoke token sent in auth.header.POST /<DRAFT_RECORD_PATH>/create_tokenCreate new token for upload file to draft record.GET /<DRAFT_RECORD_PATH>/access-tokensList tokens created for uploading to draft record.DELETE /<DRAFT_RECORD_PATH>/access-tokens/<TOKEN_ID>Revoke token by token_id...
Copyright (C) 2021 CESNET.OARepo-tokens is free software; you can redistribute it and/or
modify it under the terms of the MIT License; see LICENSE file for more
details.ChangesVersion 0.1.0 (released TBD)Initial public release.Version 0.1.2Create_token API call rewrited using oarepo-actions.Version 0.1.3revoke_token impl.permissions for create_token action addedsleeps on calls w. invalid token addedVersion 0.1.4get_by_uuid returns all; disable token_detailVersion 0.1.5pid_type test fixedVersion 0.1.6versioning typo fixedVersion 0.1.7added permission check on create_tokenfix: used canonical_url instead of url_forVersion 0.1.8oarepo-actions added to requires + flask ver.limitedVersion 0.1.9celery task; cleanup script; fixes;Version 0.1.10fix in tests
Package: oarepo-tools
oarepo-toolsInstallationAddoarepo-toolsto 'dev' dependencies section of your setup.cfgUsagemake-translationsTo have unified translations across your javascript (+react) and python sources, add the following
configuration to yoursetup.cfg:[oarepo.i18n]# List of language codes for which translations should be managedlanguages=csen# List of paths to scan for babel messages - python files and jinja templates are scannedbabel_source_paths=oarepo_oaipmh_harvester/oai_harvesteroarepo_oaipmh_harvester/oai_runoarepo_oaipmh_harvester/oai_recordoarepo_oaipmh_harvester/oai_batchoarepo_oaipmh_harvester/ui# extra message catalogues - if you use oarepo-model-builder to generate models, add the generated# translations directories herebabel_input_translations=oarepo_oaipmh_harvester/oai_harvester/translationsoarepo_oaipmh_harvester/oai_run/translationsoarepo_oaipmh_harvester/oai_record/translationsoarepo_oaipmh_harvester/oai_batch/translations# List of paths to scan for i18next messages - javascript and jsx files are scannedi18next_source_paths=oarepo_oaipmh_harvester/ui/oai_harvester/theme/assets/semantic-ui/jsoarepo_oaipmh_harvester/ui/oai_run/theme/assets/semantic-ui/jsoarepo_oaipmh_harvester/ui/oai_batch/theme/assets/semantic-ui/jsoarepo_oaipmh_harvester/ui/oai_record/theme/assets/semantic-ui/js# List of extra i18next message catalogues that should be merged intoi18next_input_translations=ui/docs-app/docs_app/theme/assets/semantic-ui/translations/docs_app/messages# this is the location where python translations are generated.babel_output_translations=oarepo_oaipmh_harvester/translations# this is the location where javascript translations are generated. Add this directory to webpack# aliasesi18next_output_translations=oarepo_oaipmh_harvester/ui/theme/assets/semantic-ui/translations[options.entry_points]# Do not forget to add this directory to your package data and `invenio_i18n.translations` entry point# - without the 'translations' suffixinvenio_i18n.translations=oarepo_oaipmh_harvester=oarepo_oaipmh_harvesterTo extract translation keys from your sources, runmake-translationsinside your module.
This will generatepofiles inside thebabel_output_translationsdirectory.Open the generatedpofiles, either withpoeditor directly, and translate the messages.Then runmake-translationsagain. This will both compile the translations for python sources
and generate thejsonfiles for javascript sources.
Package: oarepo-vocabularies
OARepo vocabulariesThis is an extension of invenio vocabularies that allows:Use the vocabularies with custom fieldsTreat vocabulary items as hierarchyInstallationAddoarepo-runtime,oarepo-vocabulariesto your virtualenv an set up the following in yourinvenio.cfg:fromoarepo_vocabularies.services.configimportVocabulariesConfigfromoarepo_vocabularies.resources.configimportVocabulariesResourceConfigVOCABULARIES_SERVICE_CONFIG=VocabulariesConfigVOCABULARIES_RESOURCE_CONFIG=VocabulariesResourceConfigDocumentationSeeNRP documentationfor more details.AuthoritiesIt is possible to provide authority sources for vocabularies.
Configuration:VOCABULARY_TYPE_METADATA={"funding":{# vocabulary of funding"name":{"en":"Funding"},"authority":FundingService}}where:fromoarepo_vocabularies.authoritiesimportAuthorityServiceclassFundingService(AuthorityService):defsearch(self,query=None,page=1,size=10,**kwargs):# performs an API and returns a listing# of serialized vocabulary items, for example:return{'hits':{'total':2,'hits':[{"id":"03zsq2967","title":{"en":"Funding 1"}},{"id":"a4gfhtt56","title":{"en":"Funding 2"}}]},# optional pagination links here}defget(self,item_id,*,uow,value,**kwargs):# performs lookup by id and returns vocabulary metadata# in this example:returnnext(xforxinself.search()['hits']['hits']ifx['id']==item_id)OrderingThis extension supports ordering and suggestion in different languages. It is enabled by default
and enables all languages inI18N_LANGUAGES,BABEL_DEFAULT_LOCALE. Sorting bytitlesorts
by the title in the current language, suggestion suggests inidand title in the default language.
Package: oarepo-vocabularies-basic
An extension toinvenio-vocabulariesthat adds
hierarchy to vocabulary records. It consists of three packages:oarepo-vocabularies- supporting librariesoarepo-vocabularies-basic- a generated vocabulary implementation
roughly equivalent to the basic invenio-vocabularies recordoarepo-vocabularies-model-builder- an extension to oarepo
model builder to easily use vocabularies in your own metadata modelTODODocument this.
Package: oarepo-vocabularies-model-builder
An extension toinvenio-vocabulariesthat adds
hierarchy to vocabulary records. It consists of three packages:oarepo-vocabularies- supporting librariesoarepo-vocabularies-basic- a generated vocabulary implementation
roughly equivalent to the basic invenio-vocabularies recordoarepo-vocabularies-model-builder- an extension to oarepo
model builder to easily use vocabularies in your own metadata modelTODODocument this.
Package: oarepo-whitenoise
OArepo whitenoiseThis library serves static resources and index.html at runtime
using the whitenoise package. This greatly simplifies the deployment
as only one image needs to be deployed.Installationpipinstalloarepo-micro-apioarepo-whitenoiseConfigurationThis library expects the static files in/whitenoisefolder. If they are located elsewhere, set environment
variableWHITENOISE_ROOTto the correct location.UsageIn your wsgi configuration, specify:[uwsgi]module=oarepo_whitenoise.wsgi:application...as your wsgi entry point...
Copyright (C) 2021 Mirek Šimek, VŠCHT Praha.oarepo-whitenoise is free software; you can redistribute it and/or
modify it under the terms of the MIT License; see LICENSE file for more
details.ChangesVersion 0.1.0 (released TBD)Initial public release.
Package: oarpy
InstallationpipinstalloarpyDocumentationThe documentation of latest release is available at:http://oarpy.readthedocs.io/en/latest/Getting startedSubmitRun an OAR job that prints “Hello world”:fromoarpy.oarjobimportsubmitjob=submit(command='echo "Hello word"',name='helloword',project='oarpy',core=1,gpu=False,walltime={'hours':2})print(job)job.wait()ifjob.exit_code:print('Failed:\n{}'.format(job.stderr))elifjob.exit_codeisNone:print('Interrupted:\n{}'.format(job.stdout))else:print('Succes:\n{}'.format(job.stdout))job.remove_logs()Only “command” is required, all other arguments are optional. See documentation for more fine-grained control with the JobFactory and Resource classes.SearchFind a job in case you know the job ID:fromoarpy.oarjobimportJobjob=Job(1130922)print(job)Find a job in case you do not know the job ID (not all arguments are necessary):fromoarpy.oarjobimportsearchfromoarpyimporttimeutilsimportosowner=os.getlogin()start=timeutils.add(timeutils.now(),minutes=-10)jobs=search(owner=owner,start=start,name='quickstart',project='oarpy',state='Terminated')print(jobs)
Package: oas3
.. |travis-ci| image:: https://img.shields.io/travis/pinntech/oas3/master.svg?style=flat-square:target: https://travis-ci.org/#!/pinntech/oas3?branch=master.. |coveralls| image:: https://img.shields.io/coveralls/pinntech/oas3/master.svg?style=flat-square:target: https://coveralls.io/r/pinntech/oas3?branch=master.. |pypi| image:: https://img.shields.io/pypi/v/oas3.svg?style=flat-square:target: https://pypi.python.org/pypi/oas3.. |license| image:: https://img.shields.io/pypi/l/oas3.svg?style=flat-square:target: https://pypi.python.org/pypi/oas3****OAS3****|travis-ci| |coveralls| |pypi| |license|OAS3 is a parser, validator and compiler for dealing with Open APISpecification v3. This library provides an interface for working withspecifications, and loading and dumping to various locations and formats.Quickstart===========To get started load your spec, it will be validated upon dumping... code-block:: pythonfrom oas3 import Spec# Load the Spec from a filespec = Spec.from_file('./spec.json')spec = Spec.from_file('./spec.yml')# Load the Spec from a URLspec = Spec.from_url('https://raw.githubusercontent.com/OAI/OpenAPI-Specification/master/examples/v3.0/petstore.yaml')# Load the Spec from a python native dictionaryspec = Spec.from_dict(spec_dict)# A spec that is sufficiently loaded (i.e. valid OAS3) can now be dumpedspec.to_file('/tmp/spec.yaml')spec.to_dict()spec.to_json()spec.to_yaml()0.1.0 2018-10-01----------------- Pre-alpha release
Package: oasis
OASIS is a tool for evaluating binary classifiers when ground truth class
labels are not immediately available, but can be obtained at some cost (e.g.
by asking humans). The tool takes an unlabelled test set as input and
intelligently selects items to label so as to provide apreciseestimate of
the classifier’s performance, whilstminimisingthe amount of labelling
required. The underlying strategy for selecting the items to label is based on
a technique calledadaptive importance sampling, which is optimised for the
classifier performance measure of interest. Currently, OASIS supports
estimation of the weighted F-measure, which includes the F1-score, precision
and recall.Important linksDocumentation:https://ngmarchant.github.io/oasisSource:https://www.github.com/ngmarchant/oasisTechnical paper:https://arxiv.org/pdf/1703.00617.pdfExampleSee the Jupyter notebook underdocs/tutorial/tutorial.ipynb:>>> import oasis
>>> data = oasis.Data()
>>> data.read_h5('Amazon-GoogleProducts-test.h5')
>>> def oracle(idx):
>>>     return data.labels[idx]
>>> smplr = oasis.OASISSampler(alpha, data.preds, data.scores, oracle)
>>> smplr.sample_distinct(5000) #: query labels for 5000 distinct items
>>> print("Current estimate is {}.".format(smplr.estimate_[smplr.t_ - 1]))License and disclaimerThe code is released under the MIT license. Please see the LICENSE file for
details.
Package: oasisabm
OASIS v1.0.0DescriptionThis is a stable version of the OASIS activity-based framework. This repository includes the simulation code, based on the Python API of the CPLEX solver.For more details, you can find the technical reports in theLiteraturefolder, and the full documentationhere.Installation guideWe recommend creating a new environment using the provided requirements file to install the correct packages.NB: You need a valid CPLEX user license to use the solver, which can be obtainedhere.TutorialAn example of how to run the simulator is provided in thedemonotebooks. Your main function should be added in therunnerscript, which you can run by typing the following command:python runner.pyCitationIf you found this repository useful, you can acknowledge the authors by citing:Pougala, J., Hillel, T., and Bierlaire M. (2023)OASIS: Optimisation-based Activity Scheduling with Integrated Simultaneous choice dimensions, Transportation Research Part C: Emerging Technologies, vol. 155, p. 104291, DOI:https://doi.org/10.1016/j.trc.2023.104291
Package: oasis-api-client
OASIS-API-CLIENTOASIS:SaaS for ONNC (Open neural network compiler)InstallationDeveloper installpython3 -m pip install -e .Using pippip install oasis-api-clientPython API ExampleHere is an example to show how to use OASIS python APIfrom oasis.bench import login, Project
# Setup your OASIS API key
api_key = "Your API KEY"
login(api_key)

# Instantiate a projct
project = Project('experiment-1')

# Add a model and its coresponding calibration samples
project.add_model('path/to/model', 'path/to/samples')

# Compile the model and optmize to `CMSIS-NN` backend
project.compile(target='CMSIS-NN-DEFAULT')


# Save the compiled model
deployment = project.save('./output')Please Checkhttps://docs-tinyonnc.skymizer.com/index.htmlfor the full documents.
Package: oasis-deconv
OASIS: Fast online deconvolution of calcium imaging dataTools for extracting the neural activity from fluorescence calcium imaging dataThe code can be readily run on neural temporal fluorescence calcium imaging data. Please have a look at thedemo.RequirementsThe scripts were tested on Linux and MacOS (some users successfully used Windows too) with a typical numerical/scientific Python 2.7 or 3.5-3.11 installation that included the followingpython >= 2.7.11matplotlib >= 1.5.1numpy >= 1.10.2scipy >= 0.16.1cython >= 0.23.4Optionally, because not necessary for running our fast method on your own data, we further installed the following to perform the comparison with interior point methodscvxpy >= 0.3.6gurobi >= 6.5.0 (www.gurobi.com, free academic license)mosek >= 7 (https://mosek.com, free academic license)InstallationPackage basedThe easiest way to install OASIS is usingpip:pip install oasis-deconvHowever, you won't have the examples provided in the GitHub repo.Compile from sourceAlternatively you can clone the repogit clone git@github.com:j-friedrich/OASIS.git
cd OASISand install the requirements either usingpip:pip install -r requirements.txtor if you are usingconda(ormamba):conda install --file requirements.txt(ormamba install --file requirements.txt)For faster execution some functions have been written in Cython and need to be compiled by running:python setup.py build_ext --inplace(Ignore the warnings that Cython is using a deprecated Numpy API. Following thecython online docs, for the time being, it is just a warning that you can ignore.)(If the compilation fails on Windows, see Srikanth'sgist.)To clean up temporary files follow it by:python setup.py clean --allExamplesThe scripts to produce the figures and table are in the subfolder 'examples' with names obvious from the PLoS Comput Biol paper.
They can be run withipython examples/fig[1-6].py.The results of fig4 and table1 will be even better than in the paper, because the version in the master branch includes later improvements, in particularly up to an order of magnitude less computing time. The specific points in history marking the time of the publications have been tagged.To demonstrate how to use the methods on your own data, we included a demo jupyter notebook in the subfolder 'examples' as well.Other implementationsMatlabRelated packagesIn order to deal not just with temporal, but with raw spatio-temporal fluorescence data, we added OASIS also toCaImAn, the computational toolbox for large scale Calcium Imaging Analysis.ReferencesThe code accompanies a short NIPS paper and an extended journal version with full details. If you use our code in your research, please cite one of them:Friedrich J, Paninski L.Fast Active Set Method for Online Spike Inference from Calcium Imaging.In: Adv Neural Inf Process Syst. 2016; p. 1984–1992.Friedrich J, Zhou P, Paninski L.Fast Online Deconvolution of Calcium Imaging Data.PLoS Comput Biol. 2017; 13(3):e1005423.
Package: oasisis
OASIS v1.0.0DescriptionThis is a stable version of the OASIS activity-based framework. This repository includes the simulation code, based on the Python API of the CPLEX solver.For more details, you can find the technical reports in theLiteraturefolder, and the full documentationhere.Installation guideWe recommend creating a new environment using the provided requirements file to install the correct packages.NB: You need a valid CPLEX user license to use the solver, which can be obtainedhere.TutorialAn example of how to run the simulator is provided in thedemonotebooks. Your main function should be added in therunnerscript, which you can run by typing the following command:python runner.pyCitationIf you found this repository useful, you can acknowledge the authors by citing:Pougala, J., Hillel, T., and Bierlaire M. (2023)OASIS: Optimisation-based Activity Scheduling with Integrated Simultaneous choice dimensions, Transportation Research Part C: Emerging Technologies, vol. 155, p. 104291, DOI:https://doi.org/10.1016/j.trc.2023.104291
Package: oasis-workshop
Oasis Workshop 2022Quick startDownload and install thejupyterlab-desktopapplication from its GitHub pagehttps://github.com/jupyterlab/jupyterlab-desktop/releasesOnce installed, select theopen from URLoption.Copy in the raw Github URLs to load each exercise.Exercise 1-https://raw.githubusercontent.com/OasisLMF/Workshop2022/main/1_overview.ipynbExercise 2-https://raw.githubusercontent.com/OasisLMF/Workshop2022/main/2_installing-models.ipynbExercise 3-https://raw.githubusercontent.com/OasisLMF/Workshop2022/main/3_autoscalling.ipynbExercise 4-https://raw.githubusercontent.com/OasisLMF/Workshop2022/main/4_user-access-management.ipynb
Package: oaspy
oaspyoaspyis a quick-and-dirty tool to generate anOpenApi 3.xspecification from aninsomnia V4collections.Getting StartedFor more, see thedocumentation.Installationoaspyis available onPyPI:pipinstalloaspyUsageTo runoaspy, try any of the following:oaspy--helpCommandsgen::new: (in progress)Generate an OpenApi 3.x file from an Insomnia collection v4.oaspygen--helpwith the default options.oaspygen--fileInsomnia_file_v4.jsondefining the version of openapi to generate.oaspygen--fileInsomnia_file_v4.json--schemav30argumentv30refers to openapi version 3.0.xdefining the version of openapi to generate and the output file name.oaspygen--fileInsomnia_file_v4.json--outputmy_oa3_export.jsona complete version of the above.oaspygen--fileInsomnia_file_v4.json--schemav30--outputmy_oa3_export.jsoncheck::new:Validates the structure of an OpenApi file.oaspycheck--helpoaspycheck--filemy_oa3_export.jsoninfo::new:Shows information from an Insomnia v4 file.oaspyinfo--helpoaspyinfo--fileInsomnia_file_v4.jsonConfigurationoaspycan be configured through apyproject.tomlif it is used as a dev dependency in your project.If left unspecified, the default configuration is equivalent to:[tool.oaspy]title="awesome api - OpenAPI 3.0"description="my awesome api"terms-of-service="http://awesome.io/terms"contact-url="http://awesome.io"contact-email="apiteam@awesome.io"license-name="MIT"license-url="https://opensource.org/license/mit/"version="1.0.0"LicenseThis project is licensed under the terms of theMIT.license.The full text of this license can be found in theLICENSE.file.How to ContributeFor any questions, comments, suggestions or contributions, go to theissues.section.
Before opening a new issue, check the existing ones to find a solution (possibly already existing) to the problem you are facing.
Package: oast
oastThis package provides our awesome spectral toolbox for performing analysis of hyperspectral data.DevelopmentSetupIn the cloned git repository, create your virtual environment:python3 -m venv .venv
source .venv/bin/activate  # or .venv/bin/activate.fish, or whateverand install this package in "editable" mode:pip install --editable .If you get an error likeERROR: File "setup.py" not found., then upgrade pip (pip install --upgrade pip) and retry.Now, you can run the console scripts and such, with all the necessary dependencies, but the local code is still being run directly, so any changes made are immediately reflected.Building and releasing for PyPIWithbuildinstalled (pip install --upgrade build), you can then run:python3 -m buildYou now have your distribution ready (e.g. a tar.gz file and a .whl file in the dist directory), which you can upload to PyPI! Withtwineinstalled (pipx install twine), upload the distribution with:twine upload dist/*Releasing for condaThis package is distributed for conda through theconda-forgechannel. When a new version is released on PyPI, that new version should also be released on conda-forge.It is highly likely that a bot will notice the new PyPI version and create a pull request onhttps://github.com/conda-forge/oast-feedstockautomatically that can be checked and merged. This is the preferred method. If such a pull request doesn't arrive, update the feedstock manually as follows.Clone your fork of the conda feedstock (https://github.com/conda-forge/oast-feedstock) and update therecipe/meta.yamlfile to reflect the new PyPI release. If you havegrayskullinstalled (pipx install grayskull), you can generate a recipe that has the new PyPI release information withgrayskull pypi --strict-conda-forge oastand manually copy over the version, sha256, etc to therecipe/meta.yamlfile in the feedstock repository. Don't blindly copy over the whole file.Commit and push the recipe change and open a pull request tohttps://github.com/conda-forge/oast-feedstock. On the pull request, comment "@conda-forge-admin please rerender" to rerender it. Once the pull request is merged, conda-forge should build and publish the new version.