Saravanan666/pypi-org huggingface dataset:
Package: openpeerpower
Open source home automation that puts local control and privacy first. Powered by a worldwide community of tinkerers and DIY enthusiasts. Perfect to run on a Raspberry Pi or a local server.Check outopenpeerpower.iofora
demo,installation instructions,tutorialsanddocumentation.Featured integrationsThe system is built using a modular approach so support for other devices or actions can be implemented easily. See also thesection on architectureand thesection on creating your own
components.If you run into issues while using Open Peer Power or during development
of a component, check theOpen Peer Power help sectionof our website for further help and information.
Package: open-petro-elastic
open_petro_elasticA Python library for petro-elastic modelling. It contains aMaterialclass for representing rocks and fluids, as well as various rock physics models and algorithms such as Hashin-Shtrikman bounds and Gassmann fluid substitution.Installationpipinstallopen_petro_elasticDevelopers and contributors can download the repository and dopip install ".[dev,test,docs]"to install the package with all its dependencies for development, testing, and building the docs.UsageThe tool can be used as a library, or from the command line usingYAMLconfiguration files. For example, see thetutorial config file.Generally, theopen_petro_elastic.materialmodule is used for interfacing withopen_petro_elasticas a Python library:>>>fromopen_petro_elastic.materialimportMaterial>>>fromopen_petro_elastic.material.sandstoneimporthertz_mindlin>>>mineral=Material(bulk_modulus=1e9,shear_modulus=1e9,density=1000)>>>sand=hertz_mindlin(mineral,porosity=0.4,pressure=1e6)>>>print(sand.density)600.0To get help on the command line interface:open_petro_elastic--helpSeethe docsfor more usage instructions.Run testsDevelopers and contributors should install everythingtest_requirements.txt. Then tests can be run with:pytestDevelopers should also intall everything indoc_requirements.txtand read theCode of Conduct.
Package: openpgpcard-x25519-agent
OpenPGP Card X25519 AgentSocket interface to Curve25519 ECDH from an OpenPGP card, using theSSH agent protocol. It's intended to be used with theOpenPGP Card WireGuard Goclient, allowing a WireGuard private key to be stored on an OpenPGP card.See theOpenPGP Card WireGuard Guidefor a complete walkthrough of installation and usage of both agent and client.DevelopmentPrerequisitesRequires Python 3.8 or newer, and thepcsc-litedaemon.Install prerequisites on Debian with the following packages:apt install gcc libpcsclite-dev make pcscd python3-dev python3-venv swigOr on Fedora:dnf install findutils gcc make pcsc-lite pcsc-lite-devel python3-devel swigSet up dev envCreate a virtualenv withpyenv:pyenv virtualenv 3.8.16 openpgpcard-x25519-agentActivate the virtualenv:pyenv local openpgpcard-x25519-agentInstall tox:pip install toxInstall pre-commit and pre-push hooks:tox exec -e lint -- pre-commit installDev tasksList all tox tasks you can run:tox listRun unit tests in watch mode:tox -e watchRun linting:tox -e lintDev usageRun agent listening at/var/run/wireguard/agent0:sudo mkdir -p /var/run/wireguard && sudo chown $USER /var/run/wireguard
tox -e agent -- -l -vvOr run agent listening on test socket:tox -e agent -- -l -s test.socket -vvPrompt to cache PIN on agent:tox -e client -- -p -t -vvClear PIN from agent listening on test socket:tox -e client -- -c -s test.socket -vvBewareAny client with access to the socket on which the agent is listening has full use of your OpenPGP card's decryption key when the agent has the card's PIN cached. An adversary with access to the socket can easily decrypt your WireGuard traffic, or impersonate your WireGuard identity; she also can decrypt regular OpenPGP messages encrypted for your card's decryption key.Use of the agent requires the OpenPGP card's PIN to be cached in memory. After the card's PIN has been cached, if an adversary is able to dump your computer's memory, she will be able to recover the PIN.Even after you clear the PIN or shut down the agent, there still may be copies of the PIN in memory that an adversary could recover.ContributingAsk questions or send patches tohttps://lists.sr.ht/~arx10/openpgpcard-x25519-agentFile issues athttps://todo.sr.ht/~arx10/openpgpcard-x25519-agentSync the latest source code withhttps://git.sr.ht/~arx10/openpgpcard-x25519-agentInstall the latest release fromhttps://pypi.org/project/openpgpcard-x25519-agent/LicenseCopyright (c) 2023 Arcem Tene, Inc.This 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: openpgp-requests
openpgp-requestsA python v3 wrapper to the inspirational "requests" module that adds some OpenPGP features.Of course, using this requires a server-side component, which you can find
on thepython-flask-restful-openpgp-proxygithub repo.IntroductionThis repository contains the code for the python v3 package
openpgp-requests, which is useful if you want to encrypt/sign/verify/decrypt
API requests/responses.The code is functional, but not strongly designed with security in mind.  It
is a Proof of Concept which I expect people with more time than myself to
improve upon or be inspired by.The server has better security for the passphrase, check it out.ExamplesPlease checkthe examples folder in this repo.Cheers.
Buanzo.
Package: openphoton
openphotonStep-By-Step Tutorial:https://youtu.be/bz9cDEuyxx0FEATURES:Light propagation using Rayleigh-Sommerfeld Diffraction IntegralIncludes Fresnel Approximation and Fraunhofer ApproximationSimulation of converging lens and diverging LensSimulation of amplitude-based test object using SLMThe latest version allows you to simulate light propagation from a laser, passing through lens, and passing through your test object. More features will be added soon.Examples of How To Use (Alpha Version)Add openphoton to your operating system or python virtual environment :pipinstallopenphotonCreate a laser beam :importopenphotonasop# side length (m)# aperture radius (m)u0=op.devices.laser_beam(side_length=0.06,aperture_radius=0.026,pixels=1024)In order to forward propagate the wave field, you must choose between fresnel (near-field) approximation and
fraunhoffer (far-field) approximation. To determine which approximation is best for your system, you have to calculate
the Fresnel number F_N. If F_N = [1, +infinity], then use fresnel approximation. Otherwise, use fraunhoffer approximation.# uo = wave field to propagate# L = source plane side length (m)# wavelength = wavelength of light (m)# z = propagation distance (m)# u1 = resulting wave field after propagationu1=op.rayleigh_sommerfeld.fresnel_approx(u0,L,wavelength,z)Apply converging lens or diverging lens on the laser beam :importnumpyasnp# u1 = wave field before the lens# L = u1 side length (m)# wavelength of light (m)# f_length = lens focal length (m)# u2 = wave field after the lensu2=np.multiply(u1,op.lenses.converging_lens(u1,L,wavelength,f_length))Apply SLM or test object on the laser beam :importnumpyasnp# filename = image of test object file namefilename:str="USAF_1951_1024p.png"# SLM_amplitude() converts RGB image into numpy array# pixel_size = number of pixels of image, ideally this must be the same with u1test_object=op.devices.SLM_amplitude(filename,pixel_size)# u1 = wave field before the test object# L = u1 side length (m)# wavelength of light (m)# u2 = wave field after the test objectu2=np.multiply(u1,test_object)References:Shen, Fabin, and Anbo Wang. "Fast-Fourier-transform based numerical integration method for the Rayleigh-Sommerfeld diffraction formula." Applied optics 45, no. 6 (2006): 1102-1110.Schmidt, Jason D. "Numerical simulation of optical wave propagation with examples in MATLAB." SPIE (2010).Voelz, David G., and Michael C. Roggemann. "Digital simulation of scalar optical diffraction: revisiting chirp function sampling criteria and consequences." Applied optics 48, no. 32 (2009): 6132-6142.
Package: openpifpaf
openpifpafContinuously tested on Linux, MacOS and Windows:New2021 paper:OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal AssociationSven Kreiss,Lorenzo Bertoni,Alexandre Alahi, 2021.Many image-based perception tasks can be formulated as detecting, associating
and tracking semantic keypoints, e.g., human body pose estimation and tracking.
In this work, we present a general framework that jointly detects and forms
spatio-temporal keypoint associations in a single stage, making this the first
real-time pose detection and tracking algorithm. We present a generic neural
network architecture that uses Composite Fields to detect and construct a
spatio-temporal pose which is a single, connected graph whose nodes are the
semantic keypoints (e.g., a person's body joints) in multiple frames. For the
temporal associations, we introduce the Temporal Composite Association Field
(TCAF) which requires an extended network architecture and training method
beyond previous Composite Fields. Our experiments show competitive accuracy
while being an order of magnitude faster on multiple publicly available datasets
such as COCO, CrowdPose and the PoseTrack 2017 and 2018 datasets. We also show
that our method generalizes to any class of semantic keypoints such as car and
animal parts to provide a holistic perception framework that is well suited for
urban mobility such as self-driving cars and delivery robots.PreviousCVPR 2019 paper.GuideDetailed documentation is in ourOpenPifPaf Guide.
For developers, there is also theDEV Guidewhich is the same guide but based on the latest code in themainbranch.ExamplesImage credit: "Learning to surf" by fotologic which is licensed underCC-BY-2.0.Created with:pip3installmatplotlibopenpifpaf
python3-mopenpifpaf.predictdocs/coco/000000081988.jpg--image-outputHere is thetutorial for body, foot, face and hand keypoints. Example:Image credit:PhotobyLokomotive74which is licensed underCC-BY-4.0.Created with:python-mopenpifpaf.predictguide/wholebody/soccer.jpeg\--checkpoint=shufflenetv2k30-wholebody--line-width=2--image-outputHere is thetutorial for car keypoints. Example:Image credit:PhotobyNinaraswhich is licensed underCC-BY-SA 4.0.Created with:python-mopenpifpaf.predictguide/images/peterbourg.jpg\--checkpointshufflenetv2k16-apollo-24-oimages\--instance-threshold0.05--seed-threshold0.05\--line-width4--font-size0Here is thetutorial for animal keypoints (dogs, cats, sheep, horses and cows). Example:python-mopenpifpaf.predictguide/imagestappo_loomo.jpg\--checkpoint=shufflenetv2k30-animalpose\--line-width=6--font-size=6--white-overlay=0.3\--long-edge=500Commercial LicenseThe open source license is in theLICENSEfile.
This software is also available for licensing via the EPFL Technology Transfer
Office (https://tto.epfl.ch/,info.tto@epfl.ch).
Package: openpifpafwebdemo
openpifpafwebdemoLinks:main repository,2021 paperandCVPR2019 paper.Live demo:https://vitademo.epfl.chServerless live demo:https://openpifpaf.github.io/openpifpafwebdemo/Use LocallyRun the full pipeline (neural network and decoder) in Python
and visualize the output in the browser:pip3installopenpifpafwebdemo
python3-mopenpifpafwebdemo.serverTroubleshooting: Make sure you are using Python3 and have the latest pip and
setuptools withpip install --upgrade pip setuptools. Do not clone this
repository. Make sure there is no folder namedopenpifpafwebdemoin your
current directory.Example:DockerFor every release, this repository builds a new docker image and uploads it
toDocker Hub: svenkreiss/openpifpafwebdemo.
You can test it with:docker run -d -p 5000:80 svenkreiss/openpifpafwebdemoThis docker images also powershttps://vitademo.epfl.ch. More info on building
the docker image and how to deploy it to Kubernetes is indocker/readme.md.APIExample using cURL:curl--data-binary@docs/me_nyc_square_500.jpeghttp://localhost:5000/v1/human-poseswhich produces:[{"coordinates":[[0.588631883263588,0.41628291457891464,3.5567557387194797],[0.621234196703881,0.36160339042544365,3.524825929280572],[0.546875,0.375,3.744302039019678],[0.6724068783223629,0.44710323959589005,3.459401266884038],[0.494683139026165,0.4611567258834839,3.5954212359489217],[0.78733691573143,0.8311769068241119,2.1321910543190827],[0.3859005756676197,0.8252473473548889,2.158424186304439],[0.0,0.0,0.0],[0.0,0.0,0.0],[0.0,0.0,0.0],[0.0,0.0,0.0],[0.0,0.0,0.0],[0.0,0.0,0.0],[0.0,0.0,0.0],[0.0,0.0,0.0],[0.0,0.0,0.0],[0.0,0.0,0.0]],"score":0.26909651332876167}]Keep-alive connection test:curl-v--data-binary@docs/me_nyc_square_500.jpeghttp://localhost:5000/v1/human-poses--next--data-binary@docs/me_nyc_square_500.jpeghttp://localhost:5000/v1/human-poses2>&1|grep'#0'*Connectedtolocalhost(127.0.0.1)port5000(#0)*Connection#0 to host localhost left intact*Re-usingexistingconnection!(#0) with host localhost*Connectedtolocalhost(127.0.0.1)port5000(#0)*Connection#0 to host localhost left intactDevTo install from source and set up for development usepip install --editable ".[test]", install the frontend dependencies withnpm installand then create the frontend JavaScript code withnpm run build. For continuous rebuilds of the js package, usenpm run watch.Run the server process with--debugto get salted version numbers to break
the browser cache for static assets and autoreload when source files change.Citation@article{kreiss2021openpifpaf,
  title = {{OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association}},
  author = {Sven Kreiss and Lorenzo Bertoni and Alexandre Alahi},
  journal = {arXiv preprint arXiv:2103.02440},
  month = {March},
  year = {2021}
}

@InProceedings{kreiss2019pifpaf,
  author = {Kreiss, Sven and Bertoni, Lorenzo and Alahi, Alexandre},
  title = {PifPaf: Composite Fields for Human Pose Estimation},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2019}
}
Package: openpile
OpenPileOpen-source PILE software.Please seeofficial documentationfor more information.This package is an open source python library that provides users a tool box for geotechnical pile
calculations.This package allows the user to:Use basic Euler-Bernoulli beam theory (as well as Timoshenko's variation) to compute
forces, deflection of a beam (or a pile) under adequate loading and
support conditions.Use Winkler's approach of a beam (or a pile) supported by linear or non-linear lateral and/or
rotational springs to compute forces and deflection of the pile based on recognised
soil models such as the widely used traditional API models for sand and clay or more recent models that stem from the PISA joint-industry project.This library supports the following versions of python: 3.7-3.10.
Python 3.11 is not supported!SupportThis package takes time and effort. You can support by buying me a coffee.Installation InstructionsPrerequisites:a version of python is installed on your machine (supported versions: 3.7-3.10)pip is installed in your environment.pipinstallopenpileFeaturesPython 3.7-3.10 supportInteractive structure perfectly suited for Jupyter NotebookIntegrated data validation to prevent wrong inputs with pydanticvery fast computations fueled by the Numpy, Numba and Pandas librariesCalculationsBeam calculationWinkler model (i.e. beam supported by soil springs)Load-driven analysesDisplacement-driven analysesOut-of-the-box computation of individual soil springsFriendly API interface with object-oriented approachMatplotlib and Pandas libraries to facilitate post-processing of results.Please share with the communityThis library relies on community interactions. Please consider sharing a post aboutOpenPileand the value it can provide for researcher, academia and professionals.
Package: openpipe
OpenPipe Python ClientThis client allows you automatically report your OpenAI calls toOpenPipe.Installationpip install openpipeUsageCreate a project athttps://app.openpipe.aiFind your project's API key athttps://app.openpipe.ai/settingsConfigure the OpenPipe client as shown below.fromopenpipeimportOpenAIimportosclient=OpenAI(# defaults to os.environ.get("OPENAI_API_KEY")api_key="My API Key",openpipe={# Set the OpenPipe API key you got in step (2) above.# If you have the `OPENPIPE_API_KEY` environment variable set we'll read from it by default"api_key":"My OpenPipe API Key",})You can now use your new OpenAI client, which functions identically to the generic OpenAI client while also reporting calls to your OpenPipe instance.Special FeaturesTaggingOpenPipe has a concept of "tagging." This is very useful for grouping a certain set of completions together. When you're using a dataset for fine-tuning, you can select all the prompts that match a certain set of tags. Here's how you can use the tagging feature:completion=client.chat.completions.create(model="gpt-3.5-turbo",messages=[{"role":"system","content":"count to 10"}],openpipe={"tags":{"prompt_id":"counting"},"log_request":True,# Enable/disable data collection. Defaults to True.},)Should I Wait to Enable Logging?We recommend keeping request logging turned on from the beginning. If you change your prompt you can just set a newprompt_idtag so you can select just the latest version when you're ready to create a dataset.Usage with langchainAssuming you have created a project and have the openpipe key.fromopenpipe.langchain_llmimportChatOpenAIfromlangchain.promptsimportChatPromptTemplatefromlangchain.schema.runnableimportRunnableSequenceprompt=ChatPromptTemplate.from_messages([("system","Classify user query into positive, negative or neutral.",),("human","{query}"),])llm=ChatOpenAI(model="gpt-3.5-turbo")\.with_tags(chain_name="classify",any_key="some")# To provide the openpipe key explicitly# llm = ChatOpenAI(model="gpt-3.5-turbo", openpipe_kwargs={"api_key": "My OpenPipe API Key"})\#     .with_tags(chain_name="classify", any_key="some")chain:RunnableSequence=prompt|llmres=chain.invoke({"query":"this is good"})
Package: openpivgui
A GUI for Open PIVThis graphical user interface provides an efficient workflow for evaluating and postprocessing particle image velocimetry (PIV) images. OpenPivGui relies on the Python libraries provided by theOpenPIV project.InstallationLaunchingUsageVideo TutorialDocumentationContributionInstallationYou may use Pip to installOpenPivGui:pip3 install openpivguiAlternatively, Conda may also work:conda install -c conda-forge openpivguiLaunchingLaunchOpenPivGuiby executing:python3 -m openpivgui.OpenPivGuiUsagePress the button »open directory«. Choose a directory that contains PIV images. Use the »back« or »forward« button to filter the directory content, until there is a list of images in the file list on the right side of the OpenPivGui.To inspect the images, click on  the links in the file-list.Walk through the riders, select the desired functions, and edit the corresponding parameters.Press »start processing« to start the evaluation.Inspect the results by clicking on the links in the file-list.Use the »back« and »forward« buttons to inspect intermediate results.Use »dump settings« to document your project. You can recall the settings anytime by pressing »load settings«.Video tutorialLearn how to use and extend OpenPivGuiwatching a less than eight minute video tutorial.DocumentationFind thedetailed documentation on readthedocs.io.ContributionContributions are very welcome! Please follow thestep by step guide in the documentation.RelatedAlso check outJPIV.
Package: openplantbook-sdk
Open Plantbook SDK for PythonThis is an SDK to integrate withOpen PlantbookAPI.More information about Open Plantbook and documentation can be foundhere.
It requires registration and API credentials which can be generated on Open Plantbook website.SeeAPI documentationfor details about returned values by the SDK.Discordfor support and questionsInstallationpipinstallopenplantbook-sdkImport or require modulefromopenplantbook_sdkimportOpenPlantBookApiUsageSeedemo.pyLicenseMIT
Package: openplc-desktop
This is a desktop app (pyqthon3/PyQt5) to run alongside the
newopenplc-serverand REST api written in goProject HomeNoteThis project is still at alpha stageInstall PackageFrom PyPihttps://pypi.org/project/openplc-desktop/pip3 install openplc-desktop
openplc-desktop.pyInstall from git#clonewithhttpgit clone https://gitlab.com/openplcproject/openplc_desktop.git#orgitgit clone git@gitlab.com:openplcproject/openplc_desktop.git#thenruncd openplc_desktop
./openplc-desktop.py
Package: openplc-editor
IDE capable of creating programs for the OpenPLC RuntimePre-RequisitesFor linux the following packages are required:sudo apt-get install build-essential pkg-config bison flex autoconf automake libtool make libssl-dev python2.7 python-pip python-wxgtk3.0For windows:?todo?Install and run from pypi#installpip install openplc-editor#runopenplc-editor.pyFind “OpenPLC Editor” on the applications menu and launch itInstall and run git#getthecodegit clone https://gitlab.com/openplcproject/openplc_editor.git
cd openplc_editor#install./install.sh#runopenplc-editor.py
Package: openplm
OpenPLMMy personal extention of huggingface/transformersInstallpipinstallopenplmTreeopenplm/# Python package.├──constant.py# Define some constant values.├──apis# Some easy-use apis.├──metrics# Some metrics.├──models# Some transformers-style models, which is an extension of huggingface/transformers.└──utils# Some utils.docs/# Some documents.examples/# Some example projects implemented with transformers or openplm.researchs/# Some research projects implemented with transformers or openplm.tests/# Test cases.models/# Some large-size model files, which are excluded from git, e.g., *.bindata/# Some datasets.tmp/# Tmps.
Package: openplugin
OpenPluginPlease see the complete documentation here:https://openplugin.org/Summary:Using Projectpython openplugin/main.py --helppython openplugin/main.py run-plugin --openplugin manifests/sample_klarna.json --prompt sample_prompt.txt --log-level="FLOW"python openplugin/main.py run-plugin --openplugin manifests/sample_klarna.json --prompt "show me some t shirts" --log-level="INFO"Using PIPpip install openplugin
openplugin --help