Saravanan666/pypi-org huggingface dataset:
Package: ocookie
The goal of ocookie is to provide a comprehensive API for working
with cookies.cookielib provides cookie management functionality, but the client and
server parts are separate and not easily convertible from one to the other.
Using cookielib is complicated because it expects httplib request and
response objects in order to parse cookies. Lastly, cookielib’s API
itself can be improved.ocookie should satisfy the needs of any application dealing with cookies.
Specifically it aims to address the following use cases:HTTP clients/crawlers/scrapers dealing with cookiesHTTP servers dealing with cookiesHTTP proxies forwarding cookiesTest frameworks keeping track of cookies and asserting over cookiesExamining cookies returned from a single requestKeeping track of session cookies via a cookie jarMaintaining pristine lists of cookie headersAccessing cookies as dictionariesAt the same time ocookie is intended to be easy to use:Client and server interfaces are similar where it makes senseStraightforward conversions from client to server objects and vice versaClear and intuitive naming conventionsComplete documentationocookie is not intended to provide a full cookie parser, as this turns out
to be a rather complex undertaking. ocookie has a minimal parser that
will work for most web sites and applications, and for the remaining cases
ocookie will fall back on cookielib for cookie parsing.Note: API is not yet stable.TestsExecute the test suite by runningnosetests.The test suite uses some nose features and will not work with unittest alone.LicenseReleased under the 2 clause BSD license.
Package: ocopy
o/COPYA multi destination copy tool / library with source and destination verification using xxHash.Installation / UpdateWith pipIf you have Python 3 installed you can just usepip:pip3 install -U ocopyUsageCLIPythonimporttempfilefrompathlibimportPathfromtimeimportsleepfromocopy.copyimportCopyJobdefsimple_example():# For the sake of this example we will create temporary directory.# You will not be doing this in your code.withtempfile.TemporaryDirectory()astmp:tmp=Path(tmp)# Define source and destination directoriessource=tmp/"source"destinations=[tmp/"destination_1",tmp/"destination_2",tmp/"destination_3"]# Create some test contentsource.mkdir(parents=True,exist_ok=True)(source/"testfile").write_text("Some test content")# Create the copy job and wait until it is finishedjob=CopyJob(source,destinations,overwrite=True,verify=True)whilejob.finishedisnotTrue:sleep(0.1)# Print errorsforerrorinjob.errors:print(f"Failed to copy{error.source.name}:\n{error.error_message}")# Show content of the mhl filemhl_file_content=list(destinations[0].glob("**/*.mhl"))[0].read_text()print(mhl_file_content)if__name__=="__main__":simple_example()
Package: ocp-cluster-login
OCP Cluster Loginocp-cluster-loginis a Python tool designed to facilitate the login
process to OpenShift clusters. It automates the web login flow using
Selenium, making it easier to handle authentication for OpenShift
clusters.FeaturesAutomates OpenShift web login process.Supports Chrome and Firefox web drivers.Checks if a user is already logged in before attempting to log in again.Handles timeouts and browser driver errors.PrerequisitesBefore you begin, ensure you have met the following requirements: -
Python 3.6 or higher. - Selenium installed. - Selenium WebDriver for
Chrome or Firefox.Installationpipinstallocp-cluster-loginUsageocp-cluster-login[-h][-sSERVER][-k][-tTIMEOUT][-d{chrome,firefox}]The tool supports several command line arguments to customize its behavior:
--s,--server: Specify the API server URL. Defaults tohttps://api.ocp.domain.com:6443.
--k,--insecure-skip-tls-verify: Skip TLS verification if necessary.
--t,--timeout: Set the timeout for waiting for login in seconds. Default is 60 seconds.
--d,--driver: Choose the web driver. Supported drivers arechromeandfirefox. Default ischrome.ContributingWe welcome contributions to the OpenShift Interactive Login CLI Tool! If
you’d like to contribute, please follow the guidelines outlined in our
CONTRIBUTING.md file in the GitHub Repository.
Package: ocpc-py
OCPC ClassifierPrincipal Curves based Classifier in two versios: One-Class and MultiClass.
This algorithm is free for use (MIT Licence)
Please refer to our research paper: " de Melo Borges, F.E., Mota, O.F., Ferreira, D.D. et al. One-class classifier based on principal curves. Neural Comput & Applic (2023).https://doi.org/10.1007/s00521-023-08721-8"
when you use this algorithm.This current version includes the pip repo, you can install the module using the pip command:pip install ocpc-pyThe pip's link ishttps://pypi.org/project/ocpc-py/The example file shows the operation of the module in its 3 different applications.
Package: ocpdet
ocpdetOCPDet is an open-source Python package for online changepoint detection, implementing state-of-the-art algorithms and a novel approach, using ascikit-learnstyle API.This package is the outcome of my Master Thesis at Imperial College London within the MSc in Statistics, Department of Mathematics.Algorithms implemented in ocpdet areCUSUM: Cumulative Sum algorithm, proposed by Page (1954)EWMA: Exponentially Weighted Moving Average algorithm, proposed by Roberts (1959)Two Sample tests: Nonparametric hypothesis testing for changepoint detection, proposed by Ross et al. (2011)Neural Networks: Novel approach based on sequentially learning neural networks, proposed by Hushchyn et al. (2020) and extended to online context (Master Thesis)InstallationpipinstallocpdetExamplesCUSUM.ipynbEWMA.ipynbTwoSample.ipynbNeuralNetwork.ipynbHow to cite this workHere is a suggestion to cite this GitHub repository:Victor Khamesi. (2022). ocpdet: A Python package for online changepoint detection in univariate and multivariate data. (Version v0.0.5). Zenodo.https://doi.org/10.5281/zenodo.7632721And a possible BibTeX entry:@software{victor_khamesi_2022,
  author       ={Victor Khamesi},
  title        ={ocpdet: A Python package for online changepoint detection in univariate and multivariate data.},
  month        = oct,
  year         = 2022,
  publisher    ={Zenodo},
  version      ={v0.0.5},
  doi          ={10.5281/zenodo.7632721},
  url          ={https://doi.org/10.5281/zenodo.7632721}}LicenseThe non-software content of this project is licensed under aCreative Commons Attribution 4.0 International License, and the software code is licensed under theBSD-2 Clause license.
Package: ocpeasy
OCPeasy - Command Line InterfaceIntroductionOCPeasy consists in a CLI to facilitate the deployment of OpenShift applications, generating the configuration based on your project requirements.Pre-requisites (Development)Poetry is required to develop and test locally the CLI.curl-sSLhttps://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py|python-AppendixThe default install poetry location is~/.poetry/bin/poetryIn order to add poetry binary to your $PATH environment variable, add the line below into your.zshrc(or equivalent)export PATH=$PATH:$HOME/.poetry/binpre-commit is required to ensure linting (with flake8) and formatting (with black) are applied before each commitmake config_precommitGet startedPrerequisites (end user):occorresponding to the version used by OpenShift server (https://docs.openshift.com/container-platform/4.1/release_notes/versioning-policy.html)User guide:https://www.ocpeasy.orgRoadmapConfiguring Tests/LintingGenerate Project yamlocpeasy.ymlGenerate Stage folder<rootProject>/<.ocpeasy>/<stage>/[stagesFiles].ymlSupporting CLI invocation fromocpeasydirectlySchema based validationSupporting environment variablesComposing existing stages with modules (e.g.: Databases, Caches, Messaging Queue, other applications etc...)Support SSH Keys for cloning (read:https://stackoverflow.com/questions/28291909/gitpython-and-ssh-keys)Changelog8MAR2021 - Configure Dockerfile path from CLI2MAR2021 - Initial CLI VersionLicenseCopyright 2021 ocpeasyPermission 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.
Package: ocpexplore
OCPExplorePython Boilerplate contains all the boilerplate you need to create a Python package.Free software: MIT licenseDocumentation:https://ocpexplore.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.3.0 (2020-04-16)First release on PyPI.
Package: ocp-freecad-cam
Overviewoc-freecad-cam exposes FreeCAD's Path workbench in a fluent python API that takes
OCP TopoDS_Shape objects and their wrappers from CadQuery and Build123d to enable generating
parametric tool paths from the comfort of your keyboard.⚠ NOTE ⚠This project is fairly experimental at this stage. Expect bugs and always
double-check the generated gcode for naughty surprises.UsageSee documentation athttps://ocp-freecad-cam.readthedocs.io/en/latest/Installationocp-freecad-cam does not attempt to install runtime dependencies. Your environment must have available
CadQuery and/or Build123d, or just OCP if you're feeling raw.ocp-freecad-cam is available on pypi:https://pypi.org/project/ocp-freecad-cam/ocp-freecad-cam requires currently a relatively fresh build of FreeCAD, ie. weekly build fromhttps://github.com/FreeCAD/FreeCAD-Bundle/releases/tag/weekly-buildsThe Path module of FreeCAD has seen major refactorings in 2023 so older versions are not compatible.Runtime dependencies summaryFreeCAD weekly 2023-06-04 or newerOCPCadQueryBuild123dDev dependeciesDev dependencies are listed in requirements-dev.txt, generated from requirements-dev.in withpip-compileUsing FreeCAD AppImageWeekly AppImage is an easy way to get started. Download the AppImage, place it in a suitable empty folder
and extract it.mkdir~/freecad
cp~/Downloads/FreeCAD_weekly-builds-33345-2023-06-04-conda-Linux-x86_64-py310.AppImage~/freecadcd~/freecad
chmod+x./FreeCAD_weekly-builds-33345-2023-06-04-conda-Linux-x86_64-py310.AppImage
./FreeCAD_weekly-builds-33345-2023-06-04-conda-Linux-x86_64-py310.AppImage--appimage-extractConfiguring FreeCAD pathsFor this module to work, it needs to be able to findFreeCAD C librariesFreeCAD Path python libraries(AppImage only?) Some other Python modules located in FreeCAD's site-packagesThis can be done by updating your virtual environment PYTHONPATH or by creating a pth file in site-packages.The three paths (also contents of the .pth file) for the above AppImage example are/home/user/freecad/squashfs-root/usr/lib
/home/user/freecad/squashfs-root/usr/Mod/Path
/home/user/freecad/squashfs-root/usr/lib/python3.10/site-packages/in a file like/home/user/miniconda3/envs/cq/lib/python3.10/site-packages/freecad.pthLimitationsPocket3D does not work, possibly related tohttps://github.com/FreeCAD/FreeCAD/issues/6815- shouldn't be a big loss
though, Surface3D can get the same things done IMO.VCarve can produce unstable toolpaths, but that is probably a bug in the underlying openvoronoi library. Tweaking the
job params may help.ContributingContributions are welcome.Missing params, fixesTestsDocumentationPR'sApply black and isort and ensure that tests pass. Preferably also include test coverage for new code.
Package: ocpm
ocpm - OpenCore Package Manager [WIP]InstallationYou have to install python >3.7 with pip first, then:pipinstallocpmUsageFirst, cd to your EFI directory, and then runocpm -Uto update all kexts.Useocpm -I [kext names...]to install kextsFeatures / To-doUpdate kexts to the latest versionInstall kextsUninstall kextsResolve dependenciesInstall specific versions of a kextInstall OS-dependent kexts (like AirportItlwm)Update OpenCore itself
Package: ocp-modules
ocp-modulesThis library provides a set of modules to assemble optimal control problems (OCPs) by generating
symbolic expressions based on casadi_.InstallationFor Usersocp-modules is (SOON) available via `pip`:

    .. code-block:: bash

        pip3 install --user ocp-modules

For DevelopersInstall :code:setuptoolsand :code:virtualenv:.. code-block:: bashpip3 install --user setuptools virtualenvCreate a new virtual environment in the foldervenvand source it:.. code-block:: bashgit clone <this_repo>
 cd <this_repo>
 python3 -m virtualenv venc
 source ./venv/bin/activateInstall ocp-modules via symlink into the virtual environment.. code-block:: bashcd <this_repo>
 pip3 install -e .A note regarding CasADiCasadi can make use of proprietary solvers such as HSL_MA57_ Follow the instructions
obtaining_HSL_ to install them and modify the solver options to use the built-in default.

Common issues with HSL solvers:

* Cannot find :code:`libhsl.so`. In this case, manually create a symlink :code:`libcoinhsl.so` ->
  :code:`libhsl.so` in :code:`/usr/lib`
* Failed to load :code:`libmetis.so`: It is hard to get a working instance of metis (the version packaged
  in Ubuntu 18.04 is not compatible). It seems to work without, but spams into stdout.

Running the examples
--------------------
Run examples via:

    .. code-block:: bash

        ./examples/<folder>/<script>.py


.. _casadi: https://web.casadi.org/
.. _sphinx: docs/build/html/index.html
.. _HSL_MA57: http://www.hsl.rl.ac.uk/catalogue/ma57.html
.. _obtaining_HSL: https://github.com/casadi/casadi/wiki/Obtaining-HSL>
Package: ocp-project-plugin
GeneralBuild ProjectTo build the project go to login in the pypi web ui and get your token. Add your token to the local pypi config.poetry config pypi-token.pypi pypi-After you made changes, change the version in the files pyproject.toml and netbox_storage/init.pyNow you can build and publish the project.poetry publish --buildUse ProjectLink:https://github.com/netbox-community/netbox-docker/wiki/Using-Netbox-Pluginsdocker-compose build --no-cache && docker-compose build --no-cache && docker-compose up -dDirectory structure+- api - The API Classes, consitsts of Serializer, URL Mapper and Views
+- filters - Filters of the models, the implementation of the method search, for searching
+- forms - The ModelForm, ModelFilterForm, ModelImportForm, ModelBulkEditForm, the forms which will be displayed
+- migrations - DB Django Migration steps
+- tables - The ModelTable, which has the configuration on how the table looks like
+- templates
  +- netbox_storage - The detail view of each model
    +- drive - The template content of drive, with base and partition model
    +- inc - The template content box in the Virtual Machine Model
    +- partition - The template content of partition, with base and physicalvolume model
    +- physicalvolume - The template content of physicalvolume with base and linuxvolume model
    +- volumegroup - The template content of volumegroup with base, logicalvolume and physicalvolume
+- views - PhysicalvolumeListView, PhysicalvolumeView, PhysicalvolumeEditView, PhysicalvolumeDeleteView, 
           PhysicalvolumeBulkImportView, PhysicalvolumeBulkEditView, PhysicalvolumeBulkDeleteViewERMQueues / Worker1. Job - add_projectGit Repo pullenÜberprüfen ob es einen Branch mit dem Ticket Namen schon gibtNeuer Branch erstellen mit dem Ticket NamenSecrets entschlüsselnOCPPRoject/AppEnvironment Model Daten in yaml konvertierenYAML Daten dem values.yaml anfügenSecrets der Secrets Datei anfügenSecret verschlüsselnMergenStatic files/opt/netbox/netbox/static/modelsPython documentationI used the Sphinx Format for the documentation of the methods and classesExport TemplatesApp Environment,Cluster Environment,Project Owner
{# Rows #}
{%- for env in queryset -%}
{{ env.app_env }},{{ env.cluster_env }},{{ env.ocp_project.project_owner }}
{% endfor %}Draw.io System Topography AlgorithmÜberprüfen ob Horizontal Pod Autoscaler verwendet wirdFalls ja variable start_pod_x setzten, x sollte unter netpool seiny_axis_k8s_start setzten: 145Anzahl replicas auslesenFalls replicas grösser als 1 dannfor 1, replicas:total_y_axis_deployment_start = total_y_axis_deployment_start + y_axis_deployment_start + (n * 90)Danach teilen durch anzahl replicas --> ergibt mittelwert auch durchschnitt genanntdiesen wert für y_axis setztenVon start_pod_x 3 * + 90 hinzufügen --> hpa setztenVon start_pod_x 3 * + 90 hinzufügen --> deployment setztenVon start_pod_x 3 * + 90 hinzufügen --> replica set setztenAnzeigen aller deployments die nicht in hpa sindAnzeigen aller replicasets die nicht in deployments sindAnzeigen aller statefullsets
Package: oc-preprocessing
OpenCitations: PreprocessThis software is meant to preprocess data dumps to be ingested in OpenCitations, provided by different data sources.
The aim of the software is that of preprocessing data dumps in order to facilitate data parsing and extraction in OpenCitations Meta and OpenCitation Index processes.
Note that preprocessing is not a mandatory step of data ingestion in OpenCitations. However, preprocessing is suggested when:A consistent part of the bibliographic entities represented in the dump come without citation dataThe dump content is redundant with respect to OpenCitations scopes (e.g.: duplicated citations retrievable both as addressed and received citations)The dump consists of a unique big file, and it is too heavy to be processed all at onceA consistent part of the data provided is not relevant with respect to OpenCitations scopes (e.g.: discipline-specific and content-related metadata)MandatoryPython 3.8+Start the tests$python-munittestdiscover-s./preprocessing/test-p"*.py"LicenseOpenCitations Index is released under theISC License.
Package: ocpsvg
OCPSVGworks at theOCPlevelusessvgpathtoolsto convert SVG path strings to and fromTopoDS_Edge,TopoDS_Wire, andTopoDS_Faceobjectsusessvgelementsto importTopoDS_WireandTopoDS_Faceobjects from an SVG documentcan be used to add SVG functionality (import and export) to higher level API
Package: ocp-vtk
OCPPython wrapper for OCCT7.5.1 generated using pywrap. Typing stubs availablehere.GoalsProvide thin bindings to OCCT.Wrap all OCCT modules (if practical).React quickly to new OCCT releases.Cater primarily for the CadQuery project.Non-goalsProvide additional functionality not present in OCCTInstallationThe easiest way to get started is to use conda:conda install -c conda-forge -c cadquery ocpBuilding from sources is also possible usinghttps://github.com/CadQuery/pywrappywrap all ocp.toml
cmake -S OCP -B build
cmake --build buildbut you might need to specify additional headers and libclang location for pywrap:pywrap -i path1/include -i path2/include -l path/to/libclang.so all ocp.toml
Package: ocpymemcache
PyMemcache Python wrapper with observability provided by OpenCensus
instrumented with tracing and metrics.The mettrics recorded include:callslatency in millisecondsMetricView NameUnitTagsLatencypymemcache/latencyms‘error’, ‘method’, ‘status’Callspymemcache/calls1‘error’, ‘method’, ‘status’Installing itpip install ocpymemcache
Package: ocqm_cohort_website
Part of our research server that we offer OCQM clients. See http://www.ocqm.org/Installation------------To use locally create a new virtual environment in an empty folder::virtualenv -p python3.4 .Activate the environment:source bin/activateAnd install the package::pip install ocqm_cohort_websiteUsage-----To see how it looks like just run::ocqm-cohort-website demoAnd open http://localhost:8000To create your own site run in an empty folder::ocqm-cohort-website initAnd start editing the `cohort.yml` file. To see your changes run::ocqm-cohort-website build --serveAnd have a look at http://localhost:8000 again.Tests-----To run the tests install tox::pip install toxAnd run it::toxocqm_cohort_website works with python 2.7 and python 3.3+Current Build Status~~~~~~~~~~~~~~~~~~~~|Build Status|.. |Build Status| image:: https://secure.travis-ci.org/seantis/ocqm_cohort_website.png:target: http://travis-ci.org/seantis/ocqm_cohort_websiteTranslations------------To add a new language::python setup.py init_catalog -l <locale>e.g.::python setup.py init_catalog -l itTo update the catalog::python setup.py update_catalogTo compile the catalog::python setup.py compile_catalogUpdating and compiling the catalog is required every time a text changes!License-------ocqm_cohort_website is licensed under MIT. See LICENSE file.Attribution-----------* The images in the example cohort are from http://pixabay.com/* The chart in the example has been generated by http://www.chartjs.org/History-------0.1.9 (2014-09-22)~~~~~~~~~~~~~~~~~~- Adds ability to define a list of portraits.[freinhard]- Adds more example content.[freinhard]0.1.8 (2014-09-19)~~~~~~~~~~~~~~~~~~- Fixed typo in German example text.[freinhard]- Added list styling including example list.[freinhard]0.1.7 (2014-08-25)~~~~~~~~~~~~~~~~~~- Make sure the *.mo files are included in the release package.[href]0.1.6 (2014-08-25)~~~~~~~~~~~~~~~~~~- Fixed bad nesting of langauges. Closes #1.[href]- Rounded corners banner image for the top only.[freinhard]0.1.5 (2014-08-21)~~~~~~~~~~~~~~~~~~- No longer compile the catalog on the fly.[href]0.1.4 (2014-08-21)~~~~~~~~~~~~~~~~~~- Fixes demo not working.[href]0.1.3 (2014-08-21)~~~~~~~~~~~~~~~~~~- Fixes http server not working under Python 3.[href]0.1.2 (2014-08-21)~~~~~~~~~~~~~~~~~~- Properly include babel commands in setup.py.[href]- Make sure all files are included in the package.[href]0.1.1 (2014-08-21)~~~~~~~~~~~~~~~~~~- Renames remaining ocqm_cohort_site references to ocqm_cohort_website.[href]0.1 (2014-08-21)~~~~~~~~~~~~~~~~- Initial release.[href]
Package: ocr4all-pylib
ocr4all-pylib - common code for ocr4all related python projectsThis package contains code used by several projects related toOCR4all.Image to label mappingFor several classifiers, the file format of training and output data is images,
while the internal format is a 2d matrix with a very small range of values
(numbers denoting different labels).The ImageMap class converts between color image representations and label matrices both ways.
It also provides JSON reading and writing of mappings between colors and labels.