Saravanan666/pypi-org huggingface dataset:
Package: oidc-provider
django-oidc-opA Django implementation of anOIDC Provider/OAuth2 ASon top ofjwtconnect.io.
This project is based onIdentityPython oidc-op.
Please consult the officialoidc-op documentationfor any further information about its features and capabilities.To configure a standard OIDC Provider you have to edit the oidcop configuration file.
Seedjango-oidc-op/oidc_provider/tests/example/example/settings.pyanddjango-oidc-op/oidc_provider/tests/example/example/oidc_provider_settings.pyto get in.Run the example demogit clone https://github.com/peppelinux/django-oidc-op.git
cd django-oidc-op

pip install -r requirements.txt
cd example

./manage.py createsuperuser

bash run.shExample Relying-PartyYou can useJWTConnect-Python-OidcRPas follow:cd JWTConnect-Python-OidcRP/example
RP_LOGFILE_NAME="./flrp.django.log" python3 -m flask_rp.wsgi ../django-oidc-op/example/data/oidc_rp/conf.django.yamlYou can also use a scripted RP handler on top of oidc-rppython3 snippets/rp_handler.py -c oidc_provider/tests/oidc_rp/conf.json -u test -p testami18 -iss django_providerConfigurationThis project relyies interely on behaviour and features provided by oidcop, to get an exaustive integration in Django it
adopts the following customizations.oidc_provider_settings.pyIn the example project it handles all the oidcop configurations.
I think it's more cleaner but feel free to adapt in your preferred schema.SettingsOIDCOP_CONFIGis a python dictionary that contains the oidcop configuration.OIDC_URL_PREFIX, eg:oidcop/if present will be used as url path in all the oidcop endpoints, except for.well-known/openid-configuration. In the example project I put this parameter inoidc_provider_settings, default is''.UserInfo endpointClaims to be released are configured inop.server_info.user_info(inoidc_provider_settings.py).
The attributes release and user authentication mechanism rely on classes implemented inoidc_op/users.py.Configuration Example:"userinfo": {
        "class": "oidc_provider.users.UserInfo",
        "kwargs": {
            # map claims to django user attributes here:
            "claims_map": {
                "phone_number": "telephone",
                "family_name": "last_name",
                "given_name": "first_name",
                "email": "email",
                "verified_email": "email",
                "gender": "gender",
                "birthdate": "get_oidc_birthdate",
                "updated_at": "get_oidc_lastlogin"
            }
        }
      }Developers'Running testsrunning tests./manage.py test oidc_providercoveragecoverage erase
coverage run manage.py test oidc_provider
coverage report -mAuthorGiuseppe De MarcoGalleryRelying-PartySession management and token preview
Package: oidcpy
OIDCPY PackageThis is a package that contains Python code to implement an OpenID Connect Provider. You can find the source code onGithub.Theauthorizemodule contains a decorator that can be applied to a Flask route handler to enforce authorization. You specify theaudienceandscopesas arguments to the decorator, like:@app.route('/',methods=['GET'])@authorize(audience='http://localhost:5000',scopes='read')defindex():pass
Package: oidia
OIDIAIntroductionOIDIA is a simple terminal-based streak tracker, written in Python usingTextual.By simple Imeansimple. There's no shaming and streak counting and all
that stuff; it just lets you record what you've done, what days you've done
it on, and how many times you did it on those days.That's it.No judgement.UsageI aim to add a help screen so that the keys etc will be visible within the
application. Hopefully though most features can be found from the footer or
are easily discovered. The main points are:aadds a new streakCtrl+ddeletes a new streakCtrl+Upmoves a streak upCtrl+Downmoves a streak downEnteredits the title of a streakSpaceor=increase the count for a dayBackspaceor-decrease the count for a day[zooms the timeline in]zooms the timeline outTODOAdd a help screenLicenceOIDIA - A simple judgement-free terminal-based streak trackerCopyright (C) 2022-2023 Dave PearsonThis program is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License as published by the Free
Software Foundation, either version 3 of the License, or (at your option)
any later version.This program is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for
more details.You should have received a copy of the GNU General Public License along with
this program. If not, seehttps://www.gnu.org/licenses/.
Package: oidv6
Download single or multiple classes from the OIDv6Release historyDocumentation in RussianInstallationpipinstalloidv6Updatepipinstall--upgradeoidv6Required packagesPackagesMin versionCurrent versionrequests2.23.0numpy1.18.4pandas1.0.4progressbar23.51.3opencv-contrib-python4.2.0.34awscli1.18.69Useful resourcesOfficial site Open Images Dataset V6List of all classes that can be downloadedClass for multiple download of the OIDv6Command line argumentsArgumentTypeDescriptionValid ValuescommandstrBoot commanddownloadercommandstrLanguageDefault value: enenru--datasetstrThe root directory for saving OIDv6Default value: OIDv6---type_datastrDatasetDefault value: traintrainvalidationtestall--classesstrSequence of class names or text file---limitintImages Upload LimitDefault value: 0 (no limit)From0to∞--multi_classesboolDownloading classes in one directoryNo value--yesboolAutomatic download metadataNo value--no_labelsboolNo labelingNo value--hide_metadataboolВывод метаданныхNo value--no_clear_shellboolDo not clean the console before runningNo valueExamplesNote!Classes that are composed of several words should be surrounded by quotation marks (if they are passed directly to the command line). For example:"Organ (Musical Instrument)"Downloading classes (apple,banana,Kitchen & dining room table) from thetrain,validationandtestsets with labels in semi-automatic mode and image limit =4(Language:Russian)CMDoidv6downloaderru--datasetpath_to_directory--type_dataall--classesapplebanana"Kitchen & dining room table"--limit4Downloading training classes (cat,dog) from thetrainset with tags in automatic mode and image limit =10(Language:English)CMDoidv6downloaderen--datasetpath_to_directory--type_datatrain--classesCatdOg--limit10--yesDownloading validation classes (see text file) from thevalidationset with labels in automatic mode and image limit =10(Language:English)Text fileperson
Organ (Musical Instrument)CMDoidv6downloader--datasetpath_to_directory--type_datavalidation--classestext_file_path--limit10--yesDownloading classes (axe,calculator) in one directory from thetrain,validationandtestsets with labels in automatic mode and image limit =12(Language:English)CMDoidv6downloader--datasetpath_to_directory--type_dataall--classesaxecalculator--limit12--multi_classes--yes
Package: oidv6-to-voc
OIDv6 To VOCConvert bounding box datasets ofOpen Images Dataset v6to VOC XML format.Installationpip3installoidv6-to-vocUsageOnce installed, you should be able to run it directly:oidv6-to-voc-hIf your shell cannot find the command, try running it with:python3-moidv6_to_voc-hCLI optionsTo start converting, you need at least a part of the images, the class names metadataandat least one of the boxes annotation CSV file:oidv6-to-voc<annotation-file(s).csv>-d<class-names-file.csv>--imgd<directory/to/your/images>--outd<your/output/diretory>About the DatasetThe Open Images V6 Dataset contains 600 classes with 1900000+ images. The images are hosted on AWS, and the CSV files can bedownloaded here.To download it in full, you'll need 500+ GB of disk space. For downloading a part of the dataset only, I would recommend theDmitryRyumin/OIDv6tool.CreditThis repo is forked fromAtriSaxena/OIDv4_to_VOC.
Package: oif
objectsInField (oif)This package generates a list of candidate detections for an input
population of moving objects in a specified list of field pointings.Requirements:python 3spiceypy python librarypyoorbpython libraryother standard python libraries like numpy, pandas, etc.NAIF SPICE UtilitiesSetup (for developers):The easiest way to get started is by using the Anaconda Python
Distribution'scondapackage managerBegin by creating and activating an environment with all the prerequisites:conda create -n oif-dev -c conda-forge -c mjuric python spiceypy openorb numpy pandas matplotlib spice-utils
conda activate oif-devThen download the various large binary files (mostly SPICE kernels) that we
don't keep in git by running./bootstrap.shNext, set up an editable (in-place) development environmentpip install -e .This will allow you to run the code from the source directory.Finally, run a test to make sure everything worked:cd test
oif input.configTo uninstall:python setup.py develop -uUsage:After installing (either the editable install withpip install -e ., or
a regular install withpip install), from the directory containing your
input configuration file run:oif input.configRefer to the documentation under thedoc/folder for more details.
Package: oiffile
Oiffile is a Python library to read image and metadata from Olympus Image
Format files. OIF is the native file format of the Olympus FluoView(tm)
software for confocal microscopy.There are two variants of the format:OIF (Olympus Image File) is a multi-file format that includes a main setting
file (.oif) and an associated directory with data and setting files (.tif,
.bmp, .txt, .pty, .roi, and .lut).OIB (Olympus Image Binary) is a compound document file, storing OIF and
associated files within a single file.Author:Christoph GohlkeLicense:BSD 3-ClauseVersion:2023.8.30QuickstartInstall the oiffile package and all dependencies from thePython Package Index:python -m pip install -U oiffile[all]View image and metadata stored in a OIF or OIB file:python -m oiffile file.oifSeeExamplesfor using the programming interface.Source code and support are available onGitHub.RequirementsThis revision was tested with the following requirements and dependencies
(other versions may work):CPython3.9.13, 3.10.11, 3.11.5, 3.12rc, 64-bitNumPy1.25.2Tifffile2023.8.30Revisions2023.8.30Fix linting issues.Add py.typed marker.Drop support for Python 3.8 and numpy < 1.22 (NEP29).2022.9.29Switch to Google style docstrings.2022.2.2Add type hints.Add main function.Add FileSystemAbc abstract base class.Remove OifFile.tiffs (breaking).Drop support for Python 3.7 and numpy < 1.19 (NEP29).2021.6.6Fix unclosed file warnings.2020.9.18Remove support for Python 3.6 (NEP 29).Support os.PathLike file names.Fix unclosed files.2020.1.18Fix indentation error.2020.1.1Support multiple image series.Parse shape and dtype from settings file.Remove support for Python 2.7 and 3.5.Update copyright.NotesThe API is not stable yet and might change between revisions.No specification document is available.Tested only with files produced on Olympus FV1000 hardware.ExamplesRead the image from an OIB file as numpy array:>>> image = imread('test.oib')
>>> image.shape
(3, 256, 256)
>>> image[:, 95, 216]
array([820,  50, 436], dtype=uint16)Read the image from a single TIFF file in an OIB file:>>> with OifFile('test.oib') as oib:
...     filename = natural_sorted(oib.glob('*.tif'))[0]
...     image = oib.asarray(filename)
>>> filename
'Storage00001/s_C001.tif'
>>> image[95, 216]
820Access metadata and the OIB main file:>>> with OifFile('test.oib') as oib:
...     oib.axes
...     oib.shape
...     oib.dtype
...     dataname = oib.mainfile['File Info']['DataName']
'CYX'
(3, 256, 256)
dtype('uint16')
>>> dataname
'Cell 1 mitoEGFP.oib'Extract the OIB file content to an OIF file and associated data directory:>>> import tempfile
>>> tempdir = tempfile.mkdtemp()
>>> oib2oif('test.oib', location=tempdir)
Saving ... done.Read the image from the extracted OIF file:>>> image = imread(f'{tempdir}/{dataname[:-4]}.oif')
>>> image[:, 95, 216]
array([820,  50, 436], dtype=uint16)Read OLE compound file and access the ‘OibInfo.txt’ settings file:>>> with CompoundFile('test.oib') as com:
...     info = com.open_file('OibInfo.txt')
...     len(com.files())
14
>>> info = SettingsFile(info, 'OibInfo.txt')
>>> info['OibSaveInfo']['Version']
'2.0.0.0'
Package: oifits
Name:python-oifitsAuthor:Shreyas BapatWebsite:http://oifits.vlbi.software/Version:0.2.0python-oifitsis the python package for dealing with Event Horizons Telescope Data. The Data
is in infamous OIFITS format. Which is too complex to parse everytime when applying any machine learning
or deep learning model on the VLBI Data. The module provides easy access to all the data that is in the
OIFITS file. And provides a easy function to export all the data to a numpy array for making computer
scientists later spending time on the application of data science models rather than understanding the data.DocumentationComplete documentation, including a user guide and an API reference, can be read on
the given link:http://oifits.vlbi.software/Requirementsoifits requires the following Python packages:NumPy, for basic numerical routinesAstropy, for fits handlingoifits is usually tested on Linux on Python
3.6 and 3.7 against latest NumPy.InstallationThe easiest and fastest way to get the package up and running is to
install python-oifits using pip by simply running:$ pip install oifitsAnd it’s done!TestingIf installed correctly, the tests can be run using pytest:$ pytest -vv
============================= test session starts ==============================
platform linux -- Python 3.7.1, pytest-4.3.1, py-1.8.0, pluggy-0.9.0
rootdir: /home/shreyas/Local Forks/python-oifits, inifile: setup.cfg
plugins: remotedata-0.3.1, openfiles-0.3.1, doctestplus-0.3.0, cov-2.5.1, arraydiff-0.3
collected 1 items
[...]
==================== 1 passed, 1 warnings in 28.19 seconds ====================
$ProblemsIf the installation fails or you find something that doesn’t work as expected,
please open an issue in theissue tracker.Contributingoifits is a community project, hence all contributions are more than
welcome! For more information, head toCONTRIBUTING.rst.SupportRelease announcements and general discussion take place on ourmailing list.
Feel free to join!https://groups.io/g/oifits-devPlease join our[matrix]channel for further queries.If you still have a doubt, write a mail directly toshreyas@einsteinpy.org.CitingIf you use oifits on your project, pleasedrop us a line.You can also use the DOI to cite it in your publications. This is the latest
one:And this is an example citation format:Shreyas Bapat et al.. (2019). oifits: oifits 0.1.1. Zenodo. 10.5281/zenodo.3402135Why care for the OIFITS now?oifits is a part of my Major Technical Project of my B.Tech. Degree. I faced the issue of not understanding
exactly what the data was, and there was NO PYTHON MODULE which can tell me what’s inside the data file.
So I spent a significant amount of my research in understanding the data. And I don’t want this to happen to
anyone who wants to dio their research using the world’s largest telescope ;) (Hypothetically!)
Package: oihelper
OI HelperYet another helper forOlympiad in Informatics!UsageDownload usingpip(make sure you have Python 3.10+ installed):$pipinstalloihelperProblem parsingOI-Helper will crawl for the specified problem and extract limitations and test samples. To do this, run:$oi-helperparseP1001# https://www.luogu.com.cn/problem/P1001This will also generate a blank template program calledP1001.cppin current folder if not exist.Running your program$oi-helperrun./P1001.cppThe above command will first look for sample cases parsed before in its database and run them. Then OI Helper will walk through the current directory and look for patterns such asP1001-1.inorP1001-1.out.Example output below.Running sample 0 ...
  AC / 0.02s / 604.0K
Running sample 1 ...
  AC / 0.01s / 476.0K
Discovered 2 local testcases in /Volumes/Data/Develop/Python/OI-Helper .
Running testcase 1 ...
  AC / 0.01s / 604.0K
Running testcase 2 ...
  AC / 0.01s / 604.0KSubmitting to OJBefore submitting, you'll need to login first:$oi-helperloginThis will ask for your credentials and a Captcha.To submit program to a problem, run:$oi-helpersubmit./P1001.cppThis will automatically submit your program to online judge. Example output below.Solution submitted with record ID 84279206.
Compiling...
Compiled successfully.
Subtask 1: AC / 0.03s / 808.0K
  #0: AC / 0.003s / 664.1K / ok accepted
  #8: AC / 0.003s / 668.0K / ok accepted
  #3: AC / 0.003s / 664.1K / ok accepted
  #6: AC / 0.003s / 668.0K / ok accepted
  #2: AC / 0.003s / 668.0K / ok accepted
  #5: AC / 0.003s / 789.1K / ok accepted
  #1: AC / 0.003s / 664.1K / ok accepted
  #4: AC / 0.003s / 668.0K / ok accepted
  #7: AC / 0.003s / 742.2K / ok accepted
  #9: AC / 0.003s / 668.0K / ok accepted
Status: Accepted / 100pts / 0.03s / 808.0KDocumentationUseoi-helper [COMMAND] --helpto see the manual.ContributingContributions are highly welcomed!LicensingOI Helper is published under the MIT license. SeeLICENSEfor details.
Package: oikonomika
Oikonomika: A Python Library for Economic AnalysisDocumentation:https://oikonomika.entelecheia.aiGitHub:https://github.com/entelecheia/oikonomikaPyPI:https://pypi.org/project/oikonomikaOikonomika is a powerful and versatile Python library designed for economic analysis, facilitating data-driven decision-making in the fields of economics, finance, and policy-making. The name Oikonomika is derived from the Greek word Οικονομικά, signifying the library's commitment to providing robust tools for studying and understanding economic phenomena.FeaturesOikonomika offers a comprehensive set of features, making it suitable for both newcomers and experienced professionals in the field of economics:User-friendly API: Oikonomika provides an intuitive API that allows users to easily access, manipulate, and analyze economic data with minimal lines of code.Wide range of economic models: The library supports various economic models, including macroeconomic, microeconomic, and econometric models, catering to diverse requirements and use cases.Data integration: Oikonomika is designed for seamless integration with popular data sources, such as FRED, World Bank, IMF, and Eurostat, enabling users to access and analyze up-to-date economic data effortlessly.Customizability: Users can create custom models, modify existing ones, and control various aspects of data preprocessing, estimation, and forecasting, ensuring tailored solutions for their specific needs.Efficient computation: Oikonomika leverages advanced algorithms and parallel processing techniques to ensure high-performance calculations, even on large-scale datasets.Visualization tools: The library includes a variety of visualization tools to help users explore and interpret the results of their economic analyses, such as time series plots, bar charts, and heat maps.Interoperability: Oikonomika is compatible with popular Python data analysis libraries, such as pandas, NumPy, and SciPy, allowing for seamless integration with existing data processing workflows and applications.InstallationYou can install Oikonomika using pip:pipinstalloikonomikaGetting StartedTo begin working with Oikonomika, visit theofficial documentationand theGitHub repositoryfor examples, tutorials, and additional information.ChangelogSee theCHANGELOGfor more information.ContributingContributions are welcome! Please see thecontributing guidelinesfor more information.LicenseThis project is released under theMIT License.
Package: oil-reservoir-synthesizer
Oil Reservoir SynthesizerA generator for synthetic oil reservoir simulator results based
onPerlin noise. The
values generated are pseudo-random but retains the nice properties
of Perlin noise.The values generated have names (such as fopr) that are derived from oil
simulators such asopm-flow.ExampleThe code exposes one class,OilSimulatorwhich is a builder of
the oil reservoir model and the generator of the values.fromoil_reservoir_synthesizerimportOilSimulatorsimulator=OilSimulator()# Build a model with one well and blocksimulator.addWell("wellName",seed=997)simulator.addBlock("5,5,5",seed=31)# Run simulationnum_steps=10fopr_values=[]# oil production rate for each time stepfortime_stepsinrange(num_steps):simulator.step(scale=1.0/num_steps)fopr_values.append(simulator.fopr())Buildingpipinstall.Testingpipinstall-e.[dev]toxtestHistoryThis project was split out ofERTandlibres.
Package: oilukla
oilukla2dOilukla - it’s easy python 2d game engine repository for your beginner’s
project’s!How to installRun your CMD as Administrator and writepip install oiluklaAnd done!InfoSiteof project! I’m on
TikTok: @mike.enemI’m from Ukraine!FinalThank’s for using my engine in your’s feature projects! Using GPL-3.0
License Hello from Ukraine
Package: oilvisco
OilViscoCalculates the blended viscosity of different motor oils, when mixed together.Example:# the script itself is called mische (german for: to mix).$mische
Whichoil?:0w20
Howmany(inl)?:3Whichoil?:10w40x1
Whichoil?:q
Done(y/n)?:y
Theresultingoilof4litershasthetyoe:2.5W25.0[//]: #( vi: set et sw=4 ts=4 ft=markdown ai : )
Package: oimdp
oimdp: OpenITI mARkdown ParserThis Python library will parse anOpenITI mARkdowndocument and return a python class
representation of the document structures.Usageimportoimdpmd_file=open("mARkdownfile","r")text=md_file.read()md_file.close()parsed=oimdp.parse(text)Parsed structurePlease seethe docs, but here are some highlights:Document APIcontent: a list of content structuresget_clean_text(): get the text stripped of markupContent structuresContentclasses contain an original value from the document and some extracted content such as a text string or a specific value.Most other structures are listed in sequence (e.g. aParagraphis followed by aLine).Lineobjects and other line-level structures are divided inPhrasePartobjects.PhrasePartare phrase-level tagsDevelopSet up a virtual environment withvenvpython3-mvenv.envActivate the virtual environmentsource.env/bin/activateInstallpythonsetup.pyinstallTestsWith the environment activated:pythontests/test.py