Saravanan666/pypi-org huggingface dataset:
Package: labgrid
Welcome to labgridPurposeLabgrid is an embedded board control python library with a focus on testing, development
and general automation.
It includes a remote control layer to control boards connected to other hosts.The idea behind labgrid is to create an abstraction of the hardware control
layer needed for testing of embedded systems, automatic software installation
and automation during development.
Labgrid itself isnota testing framework, but is intended to be combined withpytest(and additional pytest plugins).
Please seeDesign Decisionsfor more
background information.It currently supports:remote client-exporter-coordinator infrastructure to make boards available
from different computers on a networkpytest plugin to write automated tests for embedded systemsCLI and library usage for development and automationinteraction with bootloader and Linux shells on top of serial console or SSHpower/reset management via drivers for power switchesupload of binaries and device bootstrapping via USBcontrol of digital outputs, SD card and USB multiplexersintegration of audio/video/measurement devices for remote development and
testingDocker/QEMU integrationWhile labgrid is currently used for daily development on embedded boards and for
automated testing, several planned features are not yet implemented and the APIs
may be changed as more use-cases appear.
We appreciate code contributions and feedback on using labgrid on other
environments (seeContributingfor
details).
Please consider contacting us (via a GitHub issue) before starting larger
changes, so we can discuss design trade-offs early and avoid redundant work.
You can also look atIdeasfor
enhancements which are not yet implemented.DocumentationRead the DocsContributingDevelopment DocsIRC channel#labgridon libera.chat (bridged to theMatrix channel
#labgrid:matrix.org)BackgroundWork on labgrid started atPengutronixin late 2016
and is currently in active use and development.QuickstartSee theInstallation sectionfor more details.Clone the git repository:$gitclonehttps://github.com/labgrid-project/labgridCreate and activate a virtualenv for labgrid:$virtualenv-ppython3venv$sourcevenv/bin/activatevenv$pipinstall--upgradepipInstall labgrid into the virtualenv:venv$pipinstall.Tests can now run via:venv$python-mpytest--lg-env<config>
Package: labgsheet
labgsheet: Labnotes on Google SheetA python library to note ml experiments on google sheet.labgsheetprovides an easy way to note ml experiments on Google Sheet.At a GlanceYou can uselabgsheetin cosole like following:# prepare for worksheet by gspread>>>importgspread>>>fromoauth2client.service_accountimportServiceAccountCredentials>>>scope=['https://spreadsheets.google.com/feeds','https://www.googleapis.com/auth/drive']# download credentials.json previously from Google Developers Console>>>credentials=ServiceAccountCredentials.from_json_keyfile_name('credentials.json',scope)>>>gc=gspread.authorize(credentials)>>>ws=gc.create("Test for labgsheets").sheet1# note an experiment where params and a metric are used>>>fromlabgsheetimportExperiment>>>exp=Experiment(ws)>>>exp.log_multi_params({'l1':0.5,'C':10})>>>exp.log_metric('aupr',0.2345)You can also uselabgsheetinGoogle Colaboratorylike following:!pipinstalllabgsheet!pipinstall--upgrade-qgspreadfromgoogle.colabimportauthauth.authenticate_user()importgspreadfromoauth2client.clientimportGoogleCredentialsgc=gspread.authorize(GoogleCredentials.get_application_default())ws=gc.create("Test for labgsheets").sheet1fromlabgsheetimportExperimentexp=Experiment(ws)exp.log_multi_params({'l1':0.5,'C':10})exp.log_metric('aupr',0.2345)After logging, you can get a google sheet like below:InstallationTo installlabgsheet, use pipenv (or pip):$pipenvinstalllabgsheetContributionForkCreate a feature branchCommit your changesRebase your local changes against the master branchCreate new Pull RequestLicenseMITAuthorShotaro Kohama
Package: labhack
Lab HackA Roguelike Venture in Super-Science! Instead of a dungeon, we descend into a deep research facility which holds arcane military and scientific secrets. You must keep government operatives at bay while building up your array of skills and crafting abilities. Strange mutants and genetic experiments gone wrong will also plague you - you must persevere and retrieve the Documents Of Truth!LabHackInstallationRun the following to installpipinstalllabhackUsageCommand line:python main.pyIn pythonimportlabhack# Running the gamelabhack.main()# Developing LabHackToinstallLabHack,alongwiththetoolsyouneedtodevelopandruntests,runthefollowinginyourvirtualenvtoinstallthedevextras.```bash$pipinstall-e.[dev]ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseGNU
Package: labii-sdk
Labii SDKAn SDK for theLabii ELN & LIMS platformthat provides interaction with theLabii API.This package contains two modules,api_clientandsdk.Python API clients help you perform Labii API calls, such as authentication, get, patch, post, and delete. Learn more athttps://docs.labii.com/api/sdk/api-client-pythonSDK is the toolkit with prebuilt Labii components that developers use to interact with Labii APIs. Learn more athttps://docs.labii.com/api/sdk/sdk-pythonInstallpip install labii-sdkUsage# 1. Install the Labii SDKpipinstalllabii-sdk# 2. Import the packagefromlabii_sdk.sdkimportLabiiObject# 3. Initial the API objectlabii=LabiiObject()# 4. Start queryinglabii.api.login()# get a list of tableslabii.Tables.list()DocumentationLabii API documentationLabii API Client documentationLabii SDK documentationAbout LabiiLabiifacilitates research and development by providing a user-friendly, customizable Electronic Lab Notebook (ELN) and Laboratory Information Management System (LIMS) to document, manage, and interpret data. Labii ELN & LIMS can be configured for any type of data, and the functions can easily be enhanced and expanded by stand-alone applications. We address the unique needs of each of our customers and are the trusted provider of hundreds of biotech companies and academic labs.
Package: labinform
LabInform is a framework and toolchain forreproducible research, focussing on free and open-source software components. Integral parts are anelectronic lab notebook(ELN) based onDokuWiki, adatasafeas central storage for all your valuable data, andunique identifiers(Lab Object Identifier, LOI, much like the well-known DOIs) for datasets, samples, and alike.Together withASpecD(Analysis of Spectral Data), a framework for handling spectroscopic data focussing on reproducibility, it forms the basis of fully reproducible research without additional costs.For more general information on LabInform see itshomepage.InstallationInstall the package by running:pip install labinformContributeSource Code:https://github.com/tillbiskup/labinformSupportIf you are having issues, please contact the package authors via GitHub and/or file a bug report there.LicenseThe project licensed under the BSD license.
Package: labirinto-flask
Joguinho de labirinto, feito em web usando Flask como servidor, e GTM para tags.Isso é só um teste das tecnologias.Divirta-se ;]Para rodarInstale as dependências (opcionalmente usando umvirtualenv):$ pip install -r requirements.txtE rode:$ python run.pyPara rodar testes de unidadeInstale as dependências (opcionalmente usando umvirtualenv):$ pip install -r test-requirements.txtE rode:$ nosetestsDockerHá configuração para criar um container doDockerprontim pra rodar os bang.
Para criar a imagem, opcionalmente instalando os pacotes de teste automatizado:$ docker build -t <NomeDaImagem> [--build-arg TESTE=true] docker/Para rodar o programa, ou os testes, substituaOPTIONSpelas opções dodocker runeCOMANDOpor um
dos comandos citados acima:$ docker run OPTIONS <MesmoNomeDaImagem> /sbin/my_init -- COMANDO
Package: labjackcontroller
labjack-controllerAn Easy Python Wrapper for LJM to Just Take Data AlreadyThis is a package designed to make streaming data fromLabJackT-series devices easy.Device CompatibilityT7 and T7 ProT4RequirementsPython 3.5+LJMAll other requirements will be automatically acquired bypip; seerequirements.txtfor a complete list of all requirements that will be automatically obtained.InstallationYou can install this package withpipthrough ourPyPipackage with the commandpipinstalllabjackcontrollerAlternatively, since we usepbrinsetup.py, you can install from this github repository withgitclonehttps://github.com/university-of-southern-maine-physics/labjack-controller.gitcdlabjack-controller
pipinstall.Sample UsageMultiple demonstrations of library functions are located in thedemosfolder, and you canread the library documentation here.However, getting started with full streaming is as easy asfromlabjackcontroller.labtoolsimportLabjackReaderduration=10# secondsfrequency=100# sampling rate in Hzchannels=["AIN0"]# read Analog Input Channel 0voltages=[10.0]# i.e. input voltages in range -10 to +10 volts# Instantiate a LabjackReaderwithLabjackReader("T7")asmy_lj:my_lj.collect_data(channels,voltages,duration,frequency)# Get all data recorded as a 2D Numpy arraymy_data=my_lj.to_array()How To Get Help (or Help Us)If you found a bug, have a question, or otherwise need to contact us, pleaseread this.If you want to help us in the development process, or have an idea,read this.ContributorsBen MontgomerySpecial ThanksPaul Nakroshis
Package: labjack-pic
labjack_picProgram PIC microcontrollers using LabJack U3I have multiple PICkits 1, 2, and 3, but none of them can program the
part I selected for my latest project. Rather than shell out for yet
another PICkit, I wrote this script to program the part using aLabJack U3which I already had
lying around.ConnectionsConnect GND, MCLR, ICSPDAT, ICSPCLK to FIOs on the LabJack. Defaults are:MCLR on FIO4ICSPDAT on FIO5ICSPCLK on FIO6You can use different pins, just edit the script correspondingly.If your circuit is OK with 5V power and doesn't draw too much current,
you can power it with VS (USB power).  Otherwise arrange some other
power supply. LabJack outputs are 3.3 V, so lower voltages are a bad
idea.Installationpip3 install labjack_picUsagelabjack_pic [filename.hex]This programs the hex file:If the hex file contains program code, it will bulk erase that area firstIf the hex file contains config words, it will erase those firstIf the hex file contains user id words, it will erase that area firstIf the hex file contains EEPROM data, it will not erase that
first. It will update any bytes specified.Devices supportedThis script only supports low-voltage programming. This means MCLR is
not available as a general-purpose input in the circuit.  This also
means that this programmer cannot recover a part that has been
programmed to MCLR-as-input.Currently supports devices covered byProgramming Specification 40002317andProgramming Specification 40002266.These are:PIC16F17114PIC16F17115PIC16F17124PIC16F17125PIC16F17126PIC16F17144PIC16F17145PIC16F17146PIC16F17154PIC16F17155PIC16F17156PIC16F17174PIC16F17175PIC16F17176PIC16F18013PIC16F18023PIC16F18014PIC16F18024PIC16F18044PIC16F18054PIC16F18074PIC16F18015PIC16F18025PIC16F18045PIC16F18055PIC16F18075PIC16F18026PIC16F18046PIC16F18056PIC16F18076I would accept patches to support other PICs, but larger memory
devices are probably going to be obnoxiously slow to program with
LabJack hardware.PerformanceThe SPI clock of the U3 is relatively slow.  Writing takes maybe 2-3x
as long as a proper programmer that can do the full 5 MHz clock.
Package: labkit
labkit====================labkit for the calculation and analysis of bio-physical design platform.User-submitted tasks automatically in parallel.labkit also offers a number of comprehensive practical Python library, including- general (please refer to the general lib ), the framework and public utility library- compute- ensembleQuick start===============install-------simplely install labkit bysh install.shThis will install labkit and it's frontend.Preparation------------you need a linux cluster to run labkitin the cluster front you need beanstalkd and mongodb runningin the cluster front server: `labkit front`in the cluster node : `labkit worker`and all above can be configured at the first time, and then you can only use`labkit push` to submit your task. The wonderful journey is now started!Concept-------labkit use yaml config file to describe algorithm, and when your config file is written, then simplelypush it to the server, the cluster will do all other things for you and return the answer.when develop labkit, you can also easily add new modules and functions, and call them in the yaml config file.all your new module can also be deal with correctly and automatically. also all labkit's origin module are running by this way.you can easily contribute labkit so it can grow up and become more useful.workflow----------------compose a task.yml file, and the module_settings, then use```labkit push```to push a task.labkit will run the task in the cluster automatically in parallel, and finally get the answer.task.yml grammer----------------the extension of yaml config file is `.yml` and the default config file name is task.ymlif you run `labkit push` without any other arguments. `task.yml` in current folder will be push.list:- item1- item2map:key: valuerule:- a list :        // undefined key without single value will be ignored- a list item is a single task and will be evaluated sequentially.- second itemdict: function arguments, or assign a variablerepeat: 100until: conditionLearning more about labkit==========================labkit is simple and easy use! the advanced usage is also simple and powerful!It's just what you needed! The next step on your labkit journey is the `full user guide <guide/index.html>`_, where youcan learn indepth about how to use labkit and develop your own algorithm... tutorial: 本地链接和readthedocs链接. 全面的教程.. doc: 本地链接和readthedocs链接. 开发者教程.. api: 本地链接和readthedocs链接. 参考api.. labkit frontend: labkit website
Package: labm8
$python-mpipinstall-Ulabm8Copyright 2014-2020 Chris Cumminschrisc.101@gmail.com.Released under the terms of the Apache 2.0 license. SeeLICENSEfor details.Deployment InstructionsSet the desired version number in//:version.txt.Export the source tree to the publicgithub repository:$bazelrun//labm8/py:exportDeploy a new versioned release of the python package topypi:$bazelrun//labm8/py:export_python_pip----release_type=release
Package: labmachine
labmachineThis is a POC with two purposes: refactoring a cluster package fromlabfunctionsand allowing the creation and self registering of a jupyter instance.This work was inpired byLet Deep Learning VMs and Jupyter notebooks burn the midnight oil for youRight now only works for Google Cloud but should be easy to expand to other providers.For examples, seeexamplesSeeinfra_[cpu|gpu].pyandlab_[cpu|gpu].pyinfra_*files are raw implementacion of the cluster library.Lab files are abstractions built over this library for jupyter lab provisioning.FeaturesVM creation (Google)Jupyter on dockerSSL certificates (ZeroSSL & Caddy)Volumes managments (Creation, Resizing, deletion, formating, etc)DNS A record creation (Google, Cloudflare)Automatic shutdown by inactivity (by Jupyter)GPU Provisioning (nvidia-smi installation, docker configuration, etc)Linux image creation (Packer)Entities types for autocompletionLogging into cloud provider log serviceDocumentationQuickstartPermissionsVolumesNext workSeehttps://trello.com/b/F2Smw3QO/labmachine
Package: labManager-admin-server
labManager v1.0.5System for managing multi-station multi-tenant lab setups - admin-serverThis package is part of thelabManager tools, a collection of tools for managing behavioral research setups. Please seeherefor more information.InstallationpipinstalllabManager-admin-serverExample scriptAn example script for running the labManager admin-server script isprovided here.ConfigurationThe below shows the schema used for parsing the configuration file for labManager admin-server, usingStrictYAML’s notation.
Anexample configuration fileis also available.'LDAP':s.Map({# Configuration about the LDAP server and querying it'server':s.Str(),# Server FQDN or IP address'projects':s.Map({'format':s.Str(),# Regex to match projects in memberOf field of found user}),}),'toems':s.Map({# Configuration about the Theopenem instance'server':s.Str(),# Server FQDN or IP address'port':s.Int(),# Server port'images':s.Map({'format':s.Str(),# Regex to match disk images that belong to a project'file_copy_actions':s.Seq(# List of file copy actions to activate when a new disks.Map({# image is created'name':s.Str(),# Name in Theopenem of a file_copy action'partition_id':s.Int(),# Partition on the disk image for which the action should}),# be run),'standard':s.Seq(# list of "standard" disk images that should be visibles.Str(),# (read only) to all projects. Use to, for instance,),# provide a base image that new projects can start from.}),}),Secrets fileBesides the configuration yaml file, the admin-server tool also needs a secrets file containing credentials with admin privileges for the LDAP and Theopenem environments.
It should be formatted as follows:LDAP_ACCOUNT=usernameLDAP_PASSWORD=passwordLDAP_SEARCH_BASE=OU=something,DC=somethingTOEMS_ACCOUNT=usernameTOEMS_PASSWORD=passwordStandalone deploymentOne simple way to make a standalone install of the app is to downloadWinPython, e.g. the latest 3.10.x version.
I think this is recommended instead of using a system-wide or user installation of Python, so that users of the computer are unlikely to accidentally interfere with the Python distribution that runs the management tools.Do as follows:Download the dot version, not the full package, e.g.Winpython64-3.10.11.1dot.Run the downloaded exe, which unzips the WinPython files.Take the python folder from the unzipped files (e.g.python-3.10.11.amd64), you do not need the rest. This is your python distribution. Put it where you want on the disk.Open a command prompt in the root of the Python installation. Install the wanted labManager packages into it using, e.g.,.\python.exe -m pip install labManager-admin-server.Finally use thepython.exein the folder to execute your script, such theexample scriptto launch this tool.AcknowledgementsThis project was made possible by funding from theLMK foundation, Sweden.
Package: labManager-client
labManager v1.0.5System for managing multi-station multi-tenant lab setups - clientThis package is part of thelabManager tools, a collection of tools for managing behavioral research setups. Please seeherefor more information.InstallationpipinstalllabManager-client# orpipinstalllabManager-client[eye-tracker]# ①use theeye-trackerextra to enable support for reporting the status of Tobii Pro eye trackers to the labManager-master tool.Example scriptAn example script for running the labManager client script isprovided here.ConfigurationThe below shows the schema used for parsing the configuration file for labManager client, usingStrictYAML’s notation.
Anexample configuration fileis also available.'network':s.Str(),# Network on which to discover clients, e.g. 10.0.1.0/24'network_retry':s.Map({# Configuration for retrying to get network connection on startup.# Useful whenit may take some time for the network connection to# come up after the computer station starts'number_tries':s.Int(),# Number of times to try'wait':s.Int(),# Wait duration (s) between tries}),'service_discovery_protocol':# Protocol to use for client discovery, MDNS or SSDPs.Enum(['MDNS','SSDP']),s.Optional('MDNS'):s.Map({'service':s.Str(),# Service name to discover when using MDNS, e.g.,}),# _master._labManager._tcp.local.s.Optional('SSDP'):s.Map({'device_type':s.Str(),# Device type to announce and listen for when using SSDP, e.g.,}),# urn:schemas-upnp-org:device:labManagerStandalone deploymentOne simple way to make a standalone install of the app is to downloadWinPython, e.g. the latest 3.10.x version.
I think this is recommended instead of using a system-wide or user installation of Python, so that users of the computer are unlikely to accidentally interfere with the Python distribution that runs the management tools.Do as follows:Download the dot version, not the full package, e.g.Winpython64-3.10.11.1dot.Run the downloaded exe, which unzips the WinPython files.Take the python folder from the unzipped files (e.g.python-3.10.11.amd64), you do not need the rest. This is your python distribution. Put it where you want on the disk.Open a command prompt in the root of the Python installation. Install the wanted labManager packages into it using, e.g.,.\python.exe -m pip install labManager-client.Finally use thepython.exein the folder to execute your script, such theexample scriptto launch this tool.AcknowledgementsThis project was made possible by funding from theLMK foundation, Sweden.
Package: labml
Monitor deep learning model training and hardware usage from mobile.🔥 FeaturesMonitor running experiments frommobile phone(or laptop)Monitorhardware usage on any computerwith a single commandIntegrate with just 2 lines of code (see examples below)Keeps track of experiments including infomation like git commit, configurations and hyper-parametersKeep Tensorboard logs organizedSave and load checkpointsAPI for custom visualizationsPretty logs of training progressChange hyper-parameters while the model is trainingOpen source! we also have a small hosted server for the mobile web appInstallationYou can install this package using PIP.pipinstalllabmlPyTorch examplefromlabmlimporttracker,experimentwithexperiment.record(name='sample',exp_conf=conf):foriinrange(50):loss,accuracy=train()tracker.save(i,{'loss':loss,'accuracy':accuracy})PyTorch Lightning examplefromlabmlimportexperimentfromlabml.utils.lighteningimportLabMLLighteningLoggertrainer=pl.Trainer(gpus=1,max_epochs=5,progress_bar_refresh_rate=20,logger=LabMLLighteningLogger())withexperiment.record(name='sample',exp_conf=conf,disable_screen=True):trainer.fit(model,data_loader)TensorFlow 2.X Keras examplefromlabmlimportexperimentfromlabml.utils.kerasimportLabMLKerasCallbackwithexperiment.record(name='sample',exp_conf=conf):foriinrange(50):model.fit(x_train,y_train,epochs=conf['epochs'],validation_data=(x_test,y_test),callbacks=[LabMLKerasCallback()],verbose=None)📚 DocumentationPython API ReferenceSamplesGuidesAPI to create experimentsTrack training metricsMonitored training loop and other iteratorsAPI for custom visualizationsConfigurations management APILogger for stylized logging🖥 ScreenshotsFormatted training loop outputCustom visualizations based on Tensorboard logsToolsHosting your own experiments server# Install the packagepipinstalllabml-app-U# Start the serverlabmlapp-serverTraining models on cloud# Install the packagepipinstalllabml_remote# Initialize the projectlabml_remoteinit# Add cloud server(s) to .remote/configs.yaml# Prepare the remote server(s)labml_remoteprepare# Start a PyTorch distributed training joblabml_remotehelper-torch-launch--cmd'train.py'--nproc-per-node2--envGLOO_SOCKET_IFNAMEenp1s0Monitoring hardware usage# Install packages and dependenciespipinstalllabmlpsutilpy3nvml# Start monitoringlabmlmonitorOther GuidesSetting up a local Ubuntu workstation for deep learningSetting up a cloud computer for deep learningCitingIf you use LabML for academic research, please cite the library using the following BibTeX entry.@misc{labml,
 author = {Varuna Jayasiri, Nipun Wijerathne},
 title = {labml.ai: A library to organize machine learning experiments},
 year = {2020},
 url = {https://labml.ai/},
}