Saravanan666/pypi-org huggingface dataset:
Package: lab-orchestrator-ws-proxy-lib
Websocket Proxy LibLibrary that contains a Proxy for KubeVirts VNC Websockets. It uses JWT Tokens for Authentication and to transfer data.GithubPyPiRead The DocsInstallationpip3 install lab-orchestrator-ws-proxy-libDocumentationCheck out the developer documentation atwebsocketproxylib.readthedocs.io.UsageThe library contains one module called ws_proxy_lib that contains one class. This class contains arunand arun_in_threadmethod that can be used to start the proxy.First you need to initialize an object of the class. For that you need to pass some parameters:remote_url: The base URL to the Kubernetes api (for example"ws://localhost:8001")api_path: The path in the api that points to a VMI. This needs to contain the variables{namespace}and{vmi_name}(for example"/apis/subresources.kubevirt.io/v1alpha3/namespaces/{namespace}/virtualmachineinstances/{vmi_name}/vnc")local_dev_mode: This is a boolean that indicated if you are running the lib locally in a development mode or running it in a Kubernetes cluster. Running it locally disables ssl. Running it in Kubernetes will automatically include the TLS client certificate from/var/run/secrets/kubernetes.io/serviceaccount/ca.crtand use the token from/var/run/secrets/kubernetes.io/serviceaccount/token.secret_key: The key that is used to decrypt the token.After that you can just start the proxy with therunand arun_in_threadmethod. Use the parametershostandportto specify on which host and port this runs.The proxy works as follows:It creates a websocket at the given host and port. Then when you want to access a VM you need to call the websocket with a path that contains the token and the VM-name divided by a slash. Example:localhost:5001/ABCTOKENDEF/ubuntu. The example contains the tokenABCTOKENDEFand tries to access the VM with the nameubuntu. The token contains a list of allowed VM-names and if the given VM-name is part of the token you will be able to access the VM.See more at:websocketproxylib.readthedocs.io.ExamplesFor an example on how to use this library you can take a look at theWebsocketProxy Projectwhich uses this library and integrates it into a docker image.ContributingIssuesFeel free to openissues.Project StructureThesrcfolder contains the source code of the library. Thetestsfolder contains the test cases. There is a makefile that contains some shortcuts for example to run the test cases and to make a release. Runmake helpto see all targets. Thedocsfolder contains rst docs that are used inwebsocketproxylib.readthedocs.io.Developer DependenciesPython 3.8Makepip install -r requirements.txtpip install -r requirements-dev.txtReleasesYour part:Create branch for your feature (issue/ISSUE_ID-SHORT_DESCRIPTION)CodeMake sure test cases are running and add new ones for your featureCreate MR into masterIncrease version number insrc/lab_orchestrator_ws_proxy_lib/__init__.py(semantic versioning)Admin part:Check and accept MRMerge MRRunmake releaseDocsTo generate the docs run:cd docs && make html.
Package: labos
LabosEste paquete está dedicado principalmente a facilitar el uso de herramientas y paquetes utilizados en la adquisición y procesamiento de datos en el marco de los laboratorios de la carrera enCiencias Físicas de la UBA.Los paquetes hasta ahora implementados son de adquisición y análisis de datos.Análisis de datosPropagaciónEste paquete está dedicado a propagar errores utilizando una aproximación lineal sobre la formula de covarianza. El paquete está basado en las librerías numpy e sympy.fromlabos.propagacionimportPropagacion_erroresimportnumpyasnp# Formula a propagarexpr='A*cos(f*t) + C'# Las variables dependientes y sus valoresvariables=[('f',100),# Hz('A',2),# Volts('t',1),# s('C',.5),# Volts]# Los errores de las variableserrores=np.array([.0005,.0001,0,.0001]).reshape(-1,1)# Instancia de la clasepropaga=Propagacion_errores(formula=expr,variables=variables,errores=errores)propaga.fit()>>(2.224637744575368,0.0005232992460070357)AjusteEste paquete está dedicado a realizar ajustes sobre datos. El paquete está basado en las librerías numpy e sympy.Heading 3Heading 4Heading 5This is anitalictextThis is anitalictextThis is anstrongtextThis is anstrongtextThis is anstriketroghtextitem 1item 2item 3item 3.1item 3.2item 4item 1item 2item 3item 4Faztweb.comFaztweb.comthis is a quoteconsole.log('hello world')<h1>Hello world</h1>first line of code
second line of codeprint("hello world")console.log('hello world')consttest=(str)=>str+'test';<h1>Hello World</h1>ProductPricequantityLaptop3.332Mouse10.331task1[] task2[] task3task4@faztweb :+1: :smile:
Package: labpack
labPackA Collection of Methods for Data Collection & ProcessingDownloads:http://pypi.python.org/pypi/labPackSource:https://github.com/collectiveacuity/labPackDocumentation:https://collectiveacuity.github.io/labPack/IntroductionLab Pack is designed to make the process of retrieving, managing and processing data more uniform across a variety of different sources and structures. The classes and methods in this module aggregate and curate python resources and online APIs to provide a set of best practices for handling data across laboratory projects.InstallationFrom PyPi:$ pip install labpackFrom GitHub:$ git clone https://github.com/collectiveacuity/labpack
$ cd labPack
$ python setup.py installGetting StartedThis module contains a variety of classes, clients and packages for use in laboratory projects. For example to store records in an indexed file store on the local device, you can use the following methods:Create an unique ID for records:from labpack.records.id import labID

id = labID()
url_safe_id_string = id.id48
id_datetime = id.epoch
id_mac_address = id.macSave record data in local user data:from labpack.storage.appdata import appdataClient

msg_key = '%s/%s.yaml' % (id_mac_address, id_datetime)
msg_details = { 'dt': id_datetime, 'mac': id_mac_address, 'msg': 'Text me back soon' }
msg_client = appdataClient('Outgoing', 'My Team', 'My App')
mgs_client.create(msg_key, msg_details)Further ReadingFor more details about how to use labPack, refer to theReference Documentation on GitHub
Package: labpartner-uom
LabPartner - A Tool for Undergraduate Physics StudentsStruggling with Lab? Me too!Over the course of my undergraduate studies at the University of Manchester, UK, I had three years of lab. Now, as someone who teaches those labs alongside my PhD, I thought it would be useful to automate some of the more painful parts of lab, so the students can just focus on understanding the physics and assessing their results.This Python is Package can:ApplyLinear and QuadraticANYfit to Data, and Plot ResultsPropagate Errors for all functionsIt is important to acknowledge that there are other similar, very useful tools (OriginPro, LSFR, your own code). I just wanted to try making something that works as an all-in-one system, effectively acting as a Lab Partner!InstallationTo install this package, simply type:pipinstalllabpartner_uomPyPI might use labpartner-uom, but this command does the same. Someday I hope to remove the '_uom'!UsageData Analysis and PlottingThere are a few presets which allow you to auto-analyse your results.importlabpartner_uomaslplp.analyse(x,y,yerr,fit)# For data arrayslp.analyse_from_file(filename,fit)# For data in .csv files# Example with specific labelslp.analyse(t,d,derr,"Quadratic",xlabel="Time (s)",ylabel="Distance (s)")This should print the results of the fitting, as well save a .png and .pdf of the plot.Options of fits include: Linear, Quadratic, Gaussian, Exponential, Logarithmic, Sine, Cosine and more! You can also add your own functions to fit:lp.analyse(x,y,yerr,"a * exp(b * x) * sin(c * x) + d")Error PropagationTo propagate errors, simply write up the function and the variables which have uncertainties.importlabpartner_uomaslpprop=lp.errorpropagator.propagate_error(func,vars)# Exampleprop=lp.errorpropagator.propagate_error("A*x**2 + B*y + C",["x","y"])ExamplesI have given some full examples on how to use the code for a lab experiment. The first example contains a simple analysis, while the other is for free and forced oscillations, a lab I demonstrated.You can find these exampleshereAdvanced UsageThe guide above gives you a quick result. However, there is more for advanced users:Customisation of PlotsFitting without ErrorsYou can find more details on this inmore-stuff.mdSiddharth Sule, 2024Based On Tom Chen's Example Package:https://github.com/tomchen/example_pypi_package
Package: labphew
Welcome to labphew!labphewis a minimalist code and folder structure for teaching computer-controlled lab instrumentation in Python. The main purpose of Labphew is to provide a basis for those with little coding experience to build their own user-interface(s) for a piece of hardware or to control a measurement and save their date in a properly reproducible manner.Python for the LabThis project is heavily inspired by the instruction exercise written byDr. Aquiles Carattino, the mastermind behindPython for the Lab. If you want to learn more (serious!) coding for lab automation with Python, check the excellentPFTL websiteor book him for a course.Python for the Lab (PFTL) is a code architecture and a programming course for computer-controlled instrumentation. PFTL codes are designed following the MVC design pattern, splitting the code into "controller"s for defining drivers, "model"s for specifying the logic of the experiment, and "view"s for parking the GUI.
PFTL was developed byUetketo explain to researchers, through simple examples, what can be achieved quickly with little programming experience. The ultimate goal of this project is to serve as a reference place for people interested in instrumentation written in Python.The labphew packageCurrently, the labphew package contains more wishes than executable pieces. However you can use its framework as a (hopefully convenient) starting point to write your own code. Please note that this package is published underGNU General Public License v3.0. You can read all the available instruction onlabphew documentationHello world with mouse clicksThey are two recommended ways of installing labphew and using it out of the box:Installation from the Python package indexYou need to havepipinstalled.If affirmative, you can use:pip install labphewInstallation from sourceBuilding the labphew dependencies are tested on Windows and Mac PCs. It should be possible to install also on linux but we have not tested it yet.git clone https://github.com/sanlifaez/labphew.git
cd labphew
pip install .If you want to start editing or adding to the code, we recommend that you fork the repository first to your own account and install it from there. This way of installation allows you to stay connected with the  labphew repository and when needed, rebase to future releases.Owning itIf you are ready to be more engaged and adapt labphew to control your own favorite setup, please readhow to labphew.How to contributeThe labphew roadmap is still incomplete and actively discussed between its maintainers, @AronOpheij and @sanlifaez. If your interests are aligned with the main goals of this project and you want to get involved, you can send a few lines to Sanli.
Package: labpy
labPy is a collection of a laboratory instrument connectors/drivers and Qt5 (PySide2) based GUIs  / instrument widget sets
for fast data plotting, logging and instrument control. Its modular design is aiming at replacing LabView (R), but keeping it tiny and lean.source:https://gitlab.com/LARAsuite/pylablabPy main featuresunified GUI environment for easily creating lab device interfaces (python3/QT5/pyside2 based)complete application with advanced menus, toolbars, statusbar, settings dialoguesdock-able windows, flexible window movement, saving of window statessettings will be preserved over closing the appcommand-line parsingsignals for communication, threading for multi-threading applicationsmany standard widgets (Temperature display, timer, alphanumeric displays, data plotting)pluggable widget systemhardware device interface (serial communication)SiLA2 (https://sila-standard.org) supportArduino and Raspberry Pi supportextremely small footprint, fastexamples / demo librarylabpy packagescore   - labpyworkbenchwidgets - collection of reusable widgetsdev_com - device communication librarymath_models - mathematical models for data evaluation and simulationlabpy fast installationthe fastest way to install labpy is through PyPi and pip:# –user indicatates to do a local installation into home directory
pip3 install –user labpylabpy full installationgit clonehttps://gitlab.com/LARAsuite/labpy.gitcd labpy# this installs everything in a virtual environment for testing:python3 labpyinstall.py(Note: please also install dependencies !!)quick start demossee examples/demos for feature demosmore examles can be find in the examples sectiondocumentationsee docs/quickstart.rst guide for a quick startand docs for complete documentationgenerating the documentationcurrent widgetsalpha numerictimeroszillographwave generatorpump controlsimple text editpython editmatplotpyqtgraph
Package: labpython
Aboutlabpython(imported aslabpy) is a collection of modules created to simplify gathering and processing experimental data. It also features a collection of modules insidelabpy.devicesnamespace for high-level communication with various devices usingpyvisaandpydaqmxbackends. Seedocumentationfor detailed information.InstallationPackage can be installed from PyPI using:pip install labpythonOr directly from github:pip install https://github.com/franciszekjuras/labpy/tarball/masterUse[full]argument to install extra packages for data processing:pip install labpython[full]If you want to modify the package source code it's convenient to install it in developer mode. In order to do this:create and activate avirtual environment,clone or download repo from github,navigate to its main directory (the one in which thisREADME.mdresides),execute:pip install -e .now you can useimport labpyanywhere and any changes to the source code will be visible without reinstallation (if using jupyter kernel must be restarted)DocumentationRelatively recent documentation can be browsedhere.
If you want to generate documentation yourself, you can usepdoc:pip install pdoc
pdoc labpy
Package: labquest
labquestA Python module for reading fromVernier LabQuest® interfacesconnected to USB. The module has been tested on Windows 10 and macOS. The module should work with any of these LabQuest interfaces:LabQuest MiniLabQuest StreamLabQuest 3LabQuest 2LabQuestTake a look at thelabquest-examples repositoryfor ideas and a number of helpful examples.RequirementsFor Windows computers, you must have the appropriate USB drivers installed. The easiest way to do this is to run the freeVernier Graphical Analysis™installer.InstallationAutomatically install all the dependencies for USB communication.pipinstalllabquestInstallation and UsageGo to ourGetting Started with Vernier LabQuest and Python documentfor detailed information regarding installation and usage of the labquest module.LicenseGNU General Public License v3 (GPLv3)Vernier products are designed for educational use. Our products are not designed nor are they recommended for any industrial, medical, or commercial process such as life support, patient diagnosis, control of a manufacturing process, or industrial testing of any kind.
Package: labrat
A graphical color picker for the CIELAB color space that allows conversion to
and from RGB values, and adjustment of hue and saturation mapped onto the
CIELAB color space.InstallationInstallation via pip:[sudo] pip[3] install labratIf you use Arch Linux or a derivative, you may also install via theAUR package.Usageusage: labrat [-h] [--version]

optional arguments:
  -h, --help  show this help message and exit
  --version   show program's version number and exit
Package: labrepo-test
labrepo_testTesting Travis CI + codecov + automatic deploymentFree software: GNU General Public License v3Documentation:https://labrepo-test.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand thefrancois-durand/package_helperproject template.History0.1.0 (2020-05-05)First release on PyPI.
Package: labs
IntroductionLabs helps create and define (config file), execute (scale with Dask) and save (artifacts, results, metadata)
experiments.
It's main purpose is to execute ML experiments, but can be used for other use cases.Labs is using Dask delayed lazy API for distributed computation. Additionally, Scikit-Learn is also used in the
[Searcher] module.Disclaimer: Labs is currently experimental and for my own personal use.Key concepts:[Experiment Design]- a user defined experiment. The [Experiment Design] is being expressed by a func, which will be
executed by an [Experimenter/s].[Experiment]- a combination of hyper parameters to be tested while running [Experiment Design].[Experiment Run]- using the [Experiment Configuration] and [Experiment Design], numerous [Experiments] will be
executed. The [Experiment Run] will output best [Experiment] (best hyper parameters combination).[Experiment Configuration]- sets of configurations which will define the [Experiments] to be executed in Experiment
Run.[LabManager]- running all the [Experiments Configurations] as defined in a config file. A [LabManager] can perform
numerous [Experiment Configuration] and [Experiment Design][Experimenter]- an entity which perform the tuning/experimenting process.[Searcher]- an entity used by an [Experimenter] to create the [Experiments] in Experiment Run.
Example Searchers: Grid Search, Random Sampling, Bayesian Search (with the great skopt package). The [Searcher] use the
defined space in [Experiment Configuration].1. Installation processpipinstalllabs2. Docs(Documentation is not completed yet)Quick StartExperimentersSearchersLabManagerLive ReportingConfigsSuggested Steps3. FutureCurrently, the project is very new and not completed.The project need more development to support distributed computation options.
The future plan is to use Dask rich and developed ecosystem, for simple and fast development of distributed computation
options.Future developments:pytest testing.Flow options - checkpoint saving, time caps, delta improvement and more.Docker support.Kubernetes support.Experiments Artifact saved in cloud storage options.MLFlow interaction.
Package: labs-build
L.A.B.SL.A.B.S. stands for Language Agnostic Build System.This is "yet another" build system to replace cmake and meson.Labs is not meant to be "the faster" (even though its use of ninja similar to meson makes roughly as fast as meson), but to produce reproductible builds.Contrary to CMake or Meson, you don't need to learn yet another language since plain python is used.The philosophy behind Labs is that simple tasks should be trivial to do, and hard ones should be easy.A work in progressThe project is still at a draft step : Only the low-level api is developped yet, but it is on heavy developpment, thus expect additionnal feature coming fast.RoadmapNinja abstraction and build generationVariablesRule with variablesTarget files setBuild rulesExplicit, implicit and order-only targetsOveride variablesCMake-like build configurationBasic find_programFind program in pathFind program from several namesEasy for a build to depend on a found programRun found program at config timeRun found programm at ninjaCustom commands (with pipes, file redirection etc.)Command line interfaceCMake-like semantic shadow buildCMake-like incremental cacheFeature testing frameworkDocumentationIn code docstringTutorialExtension systemInstall rulesGCC compatible workflow (anything that compiles to *.o filesCompile wrapper classUnderstand common C/CXX env variablesHandle include pathsHandle libsauto_build function to compile with the right compiler automatically regarding the file ype.sub projectfind_libusing pkg_configusing CMakeJava compilationQtOther languageGoRust...Wrapper to handle linux and windows with the same commandsBuild an execurable for windows embedding the python interpreterIDE project generationAdvanced find_programRequire versionHost, build and target machin distinctionLook at python modulessupport a wide range of compilersDon't be fooled : half of the features are checked, but it actually represent at most 5% of the targeted features =))If you like the project, feel free to apply for implement some feature by creating an issue !LicenseCopyright © 2020 Léo Flaventin HauchecorneThis 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: labs-cdk
Labs CDK!This project is used to define aws cdk constructs which can be used by labs as building
blocks for creating applications.Set upInstall python3$ brew install python3Create a virtualenv.$ pip install virtualenvwrapper
$ mkvirtualenv labs-cdk --python=python3Once the virtualenv is activated, you can install the required dependencies.$ pip install -r requirements.txt
$ pip install twineInstall nodejs fromhttps://nodejs.org/en/download/Install aws cdk npm package$ npm installBuild and upload to PyPIUpdate the version number in setup.pyBuild the package$ python setup.py sdist bdist_wheelDeploy to PyPi$ twine upload -r pypi dist/labs_cdk-{version-number}*To add additional dependencies, for example other CDK libraries, just add
them to yoursetup.pyfile and rerun thepip install -r requirements.txtcommand.
Package: labscript
thelabscript suite» labscriptExpressive composition of hardware-timed experimentsThelabscriptPython library permits expressive composition of hardware-timed experiments. Hardware connections and experiment logic are described using Python classes, functions and methods, which labscript translates into low-level primitives specific to individual instruments.Installationlabscript is distributed as a Python package onPyPIandAnaconda Cloud, and should be installed with other components of thelabscript suite. Please see theinstallation guidefor details.
Package: labscript-c-extensions
thelabscript suite» labscript-c-extensionsC language extensions used by thelabscript suiteC language extensions used by thelabscript suite. We provide built distributions for these extensions as conda packages onAnaconda Cloudand wheels onPyPIfor various platforms.Bundling these extensions in a separate module ensures that developer installations of otherlabscript suitecomponents don't depend on build tools, as they can install the prebuilt wheel/conda package containing the extensions. Only developers of these extensions need the build tools (for example,MSVC++ on Windows)Installationlabscript-c-extensions is distributed as a Python package onPyPIandAnaconda Cloud, and should be installed with other components of thelabscript suite. Please see theinstallation guidefor details.