Saravanan666/pypi-org huggingface dataset:
Package: opendbm
OpenDBMSupported OS PlatformsOSBuild StatusLinuxWindowsmacOS-IntelWhat is itOpenDBM is a software package that allows for calculation of digital
biomarkers of health and functioning from video or audio of an individual’s
behavior. It integrates existing tools for behavioral measurements such as
facial activity, voice, speech, and movement into a single package for measurement
of overall behavior.More About OpenDBMAt a modular level, OpenDBM is a library that consists of the following components:ComponentDescriptionFacialAn OpenDBM module to get facial attributesMovementAn OpenDBM module to get movement attributesAcousticAn OpenDBM module to get acoustic attributesAudioAn OpenDBM module to get audio attributesUsually, OpenDBM is used for:A digital biomaker extraction appA helper tools to give insight of patient conditionTable of Contents⭐️ InstallationPrerequisitesInstall DependenciesOpenDBM Installation⭐️ UsageBasic UsageTry your first OpenDBM program⭐️ More resources⭐️ License⭐️ People⭐️ InstallationPrerequisites (Install Dependencies)With Conda Environment (recommended)Make sure to install conda first atanacondaOn Linux/macOScondainstall-cconda-forgecmakeffmpegsoxOn Windows#Make sure to run in Anaconda Prompt, or add conda to the path.condainstall-cconda-forgeffmpegsoxdlibWithout Conda EnvironmentInstallation instructionsOpenDBM InstallationpipinstallopendbmModel Download ( Facial and Movement Components)Make sure you have installed docker and already login to Docker Hub.If you haven't, Find the tutorialheredockerpullopendbmteam/dbm-openfaceBack to TOC ☝️⭐️ UsageBasic UsageTry your first OpenDBM programfromopendbmimportFacialActivity#make sure Docker is active to access the modelmodel=FacialActivity()path="sample.mp4"model.fit(path)landmark=model.get_landmark()To get the attribute like mean and std is as straighforward as.mean():landmark.mean()landmark.std()landmark.to_dataframe()# convert results as pandas dataframeFor more in-depth tutorials about OpenDBM, you can check out:Introduction to OpenDBMAdvance GuideAPI DocumentationBack to TOC ☝️⭐️ More resourcesOur wikiOpenDBM 2.0 DocumentationAiCure pageBack to TOC ☝️⭐️ LicenseOpenDBM is published under the GNU AGPL 3.0 license.⭐️ PeopleOpenDBM was developed by Anzar Abbas and Vijay Yadav, alongside Vidya Koesmahargyo and Isaac Galatzer-Levy, from within the Research and Development department at AiCure––a health tech startup in New York. It was made open source in October 2020. You can contact us atopendbm@aicure.com.Back to TOC ☝️
Package: opendc-eemm
OpenDC EEMMOpenDC Extension for Energy Modelling & ManagementDocument:https://opendc-eemm.rtfd.io.Get StartedFirst, you need to download market data in CSV format from the following offical websites:Day-ahead market data:ENTSO-EImbance market data:TenTNote that data from ENTSO-E is in CET, whilst data from TenT is in GMT. Please make sure that all market data you selected are of the same period. The./example/market/directory contains two such sample datasets.Next, to run the example, you also need the simulation results produced by theOpenDC datacenter simulator. A sample parque file can be foundhere.InstallationPlease follow the instructions presentedhere.UsageTop-level commandsusage:opendc-eemm[-v][-h]-tpath[--puefloat]{trace,market,decision}...

CLIofOpenDCExtensionforEnergyModelling&Managament.

optionalarguments:-v,--versionShowversionnumberofthepackageandexit.-h,--helpShowthehelpmessagesandexit.-tpath,--tracepathPathtosimulationresults(expectingaParquefile).--puefloatPUEvalueofthesimulatieddatacenter.

subcommands:Availablecommands.{trace,market,decision}traceVisualizesimulationresults.marketComparecostsindifferentmarkets.decisionOptimizefine-graineddecision-making.Visualize simulation resultsusage:opendc-eemmtrace[-h]-s['power','oc'][-ffloat][-gvalue]optionalarguments:-s['power','oc'],--show['power','oc']Choose'power'toshowpowerdraw;choose'oc'toshowover-commissioned.-ffloat,--frequencyfloatFrequencyofsimulatedmachines.-gvalue,--governorvalueGovernortovisualize.Analyze energy marketsusage:opendc-eemmmarket[-h]-s['load','strategy']-ofloat-dpath-ipath

optionalarguments:-s['load','strategy'],--show['load','strategy']-ofloat,--od_pricefloatOn-demandenergyprice.-dpath,--dayahead_pricespathPathtoday-aheadenergyprices(expectingaCSVfile).-ipath,--imbalance_pricespathPathtoimbalanceenergyprices(expectingaCSVfile).Invoke DVFS schedulerusage:opendc-eemmdecision[-h]-o['score','schedule'][-ffloat]-dpath-ipath-ppath-a['first','last','mean'][-spath]optionalarguments:-o['score','schedule'],--option['score','schedule']Choose'score'tocomputetheagreementaccuracy(AA)soreofthepredictions;choose'schedule'forDVFSscheduling.-ffloat,--factorfloatDampingfactoroftheDVFSscheduler.-dpath,--dayahead_pricespathPathtoday-aheadenergyprices(expectingaCSVfile).-ipath,--imbalance_pricespathPathtoimbalanceenergyprices(expectingaCSVfile).-ppath,--predictionspathMachinelearningpredictions(expectingaCSVfile).-a['first','last','mean'],--aggregate['first','last','mean']Aggregationmethodformachinelearningpredictions.-spath,--save_topathDestinationpathoftheDVFSschedule.History...
Package: opendeliverybot
Open Delivery Bot 📦🤖📦 Deliver anything, anywere! powered byMineFlayer.About•Key Features•How To Use•Install•Credits•LicenseAbout 📬Welcome to the Open Delivery Bot project! This open-source initiative empowers users to deploy a self-hosted delivery bot, offering an array of advanced functionalities. Our command-line interface (CLI) or remote web GUI allows effortless control and management, ensuring a seamless experience. 🤖Open Delivery Bot shines with its dynamic features, including pathfinding and optimized elytra flight, accelerating your delivery processes for utmost efficiency. Say goodbye to manual intervention and embrace automation! 📦Getting Started 🏁[!IMPORTANT]
Some parts of the code are still in development and do not work!All instalation instructons can be foundhere(6) Arguments & Options 📄[!IMPORTANT]
Not changing some of these settings will result in the bot not working. 🛑[!WARNING]
Do not share your Minecraft info to anyone. 🔒How to use the argumentsThe command line arguments available are:--email: The Minecraft Email the bot will log into (use a name and no password for a cracked account). 📧--password: The password for your account (if not using cracked). 🔑--host: The IP address or hostname of the Minecraft server. 🖥--port: The port number the Minecraft server is listening on. Default is 25565. ⚡--auth: The authentication method your account requires (mojang/microsoft/cracked). Default is "microsoft". 🔐--version: Minecraft version to use when connecting to a server. Default is auto. 🕹--check_timeout: How often in milliseconds the bot checks if it is still logged into the server. Default is 600000 (10 minutes). ⏱--viewer_port: The local port to run the viewer server on so you can visually see what the bot is doing. Default is 8000. 👀--quit_on_low_health: Disconect the bot if the bot is on low health. Default is "True". 👤--low_health_threashold: How low the health must be for the bot to quit. Default is "10". 👤--armor_equip: If the bot needs to equip all available armor. Default is "True". 👤🚀 GitHub Codespaces (run the bot in the cloud)[!IMPORTANT]
If the console gets spammed with random data, try changing the version from false to a version you know is supported by the server.Copy this code to you clipboard and run it once the terminal is availablepython-mopendeliverybotOpen GitHub CodeSpaces belowRoadmapAdd config.json fileAddclickChange to use as Pip package for better useAdd Discord integrationAdd Lava Caster (request by @givenbakerad on Discord)Add Custom code runner (a way for users to test their own bot code in a nice and simple way)MineFlayer 🚀Mineflayer is a complex library that allows you to control Minecraft accounts through a powerful, stable, and high-level JavaScript APIDiscordhttps://discord.gg/z8yRexNc3UCreditsMineFlayerScreenshots:DashboardSettings
Package: opendem
# dem
See test_dem.py for examples of how this is used.Collect elevation data from AWS or Thredds, merge, reproject, resample.No API key needed!pip install opendem$ python>>> import demReturn elevation, aspect, or slope, that has been correctly transformed to fit rasters you are already working on.![Montana Slope](https://github.com/dgketchum/dem/blob/master/Montana_Slope.png)Pulls a web map tile service (AWS) list of tiles, based on input zoom level and bounding box, merges it,
reprojects it, and resamples it to a raster you are working with.This is intended for work where one needs a dem (elevation), slope, or aspect in a grid that matches another
dataset they are working on.For example, I work on Landsat images that come in WGS UTM coordinate reference systems.  With [bounds](https://github.com/dgketchum/bounds),
[satellite_image](https://github.com/dgketchum/satellite_image), and [Landsat578](https://github.com/dgketchum/Landsat578), I can easily download and unzip a Landsat scene, read it into a
Landsat5 (or 7, 8) object, pass the object attributes to dem, and get back a numpy array that is reprojected
into the same local coordinate system for accurate scientific analysis with the cloud mask, reflectance, albedo and other
handy data returned by methods in satellite_image.This is intended to replace the task of downloading NED files and going through a long process in a GIS to
merge, reproject, resample, etc, which often requires time consuming file management and use of proprietary software.This pulls elevation data from the web, much like an app on your phone does.  Don’t spend all your time dealing with
NED tiles!Big ups to [rasterio](https://github.com/mapbox/rasterio).
Package: open-dev
Open_Devopen_dev is a command-line tool built to assist developers in creating pull requests with ease. The tool interacts with the OpenAI GPT-3 API to generate a summary of changes made to a repository and use it to create a pull request.RequirementsInstallation from SourceTo install open_dev, you must have Python 3.6 or later installed on your system.Clone this repository:git clone https://github.com/8ball030/open_dev.git.Navigate to the project directory:cd open_dev.Install the required dependencies:pip install -r requirements.txt.Set up yourOPENAI_API_KEYenvironment variable from OpenAI.Runpython setup.py installto install open_dev.Install from PipYou can also install open_dev from pip by running the following command:pipinstallopen_devUsageOnce open_dev is installed, you can use it by running the open-dev command in your terminal.odevrepopull--helpUsage:odevrepopull[OPTIONS]Createsapullrequestbasedonasummaryofchangesfromchatgpt.

╭─Options───────────────────────────────────────────────────────────────────╮
│--target-branch-tbTEXTThetargetbranchtocompareagainst.│
│--title-tTEXTThetitleofthePR.Ifnonewillbe│
│autogeneratedbychatgpt3.│
│--description-dTEXTThedescriptionofthePR.Ifnonewillbe│
│autogeneratedbychatgpt3.│
│--dry-runSimulatethecommitandprcreation.│
│--helpShowthismessageandexit.│
╰─────────────────────────────────────────────────────────────────────────────╯open-devrepopullYou can also specify options when running the pull command. For example, to specify a target branch, you can run:open-devrepopull--target-branchdevelopExample  outputLicense
This project is licensed under the MIT License.
Package: open-dev.to
open-dev.toOpen a new dev.to browser tab from your terminalInstallationInstall open-dev.to fromPyPipip install open-dev.toHow to use$ dev.to --help
Usage: dev.to [OPTIONS]

  Open a new dev.to browser tab on the browser

Options:
  -t, --tag TEXT  add a tag
  --help          Show this message and exit.Open home page$ dev.toOpen a tag pagee.g javascript, career, productivity$ dev.to -t javascriptEnjoy!
Package: opendiamond
Interactive search of non-indexed datahttp://diamond.cs.cmu.edu/Installing the opendiamond backend (not including C libfilter and scope cookie hook) is now purely Pythonic.condaenvcreate-n<VIRTUAL_ENV_NAME>-fenvironment.yml
condaactivate<VIRTUAL_ENV_NAME>
pythonsetup.pyinstall# start the backenddiamonddThe virtual env's Python interpreter path is "baked in" in the entry points. So one can also run directly withoutconda activate:/path_to_virtual_env/bin/diamondd
Package: opendiamond-scope
OpenDiamond-scopeLibrary and tools for manipulating OpenDiamond search scopes.To install the latest release from PyPIpipx install opendiamond-scope          # or
pip install --user opendiamond-scopeBuilding from sourceFor development, a consistent development environment is managed with poetry.
If you are developing locally it is recommended to set up pre-commit git hooks.poetry install
poetry run pre-commit install   # optionalThe code can then be run from the managed environment.poetry run opendiamond-scope -hRunning of tests and release tagging is done with nox, which should already be
installed in the development environment at this point.# run tests against different installed python interpreters
poetry run nox

# release version tagging (and publishing) are handled by a nox script
poetry run nox -s release -- [major/minor/patch]
Package: opendns
# OpenDNS for PythonSmall set of utilities to work with [OpenDNS](https://www.opendns.com/) from Python# SetupStable version:pip install opendnsDevelopment version:pip install .# UsageAs a library:from opendns import get_ip
print(get_ip())As a CLI tool:$ opendns-getip
31.217.218.30# LicenseSource code available under [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0). The services
behind [OpenDNS](https://www.opendns.com/) are available under their own terms and conditions, so respect them.
Package: opendorslib
opendorslibLibrary for creating the OpenDORS dataset.Installation# Clone git repository & change into clone directorygitclonegit@gitlab.dlr.de:drus_st/opendorslib.gitcdopendorslib# Install with poetrypoetryinstallBuild Python packageRunpoetry build.To publish to PyPI, runpoetry publish.
You need to have a PyPI API token configured to do this.Build conda packageThe conda package is configured inconda/recipe/local/meta.yaml,
and reuses information frompyproject.toml.To build package locally, run# Update to next dev version to keep build metadata intactpoetryversion0.1.dev<n>
condacreate-ncondabuildconda-buildgit
condaactivatecondabuild
condabuildconda/recipe/local<optional:--output-folder[FOLDER]># e.g.:#  conda build conda/recipe/local --output-folder /home/stephan/src/opendors/conda-pkgsYou can then install the package in a new environment and use it:condacreate-nmy-env--use-localopendorslibRun testsTests can be run locally as follows:poetryrunpython-mpytesttests/Test coverageCoverage (with branch coverage) can be displayed as follows:poetryrunpython-mpytesttests--cov=opendorslib--cov-branch--cov-report=html--cov-report=term
Package: opendp
OpenDPThe OpenDP Library is a modular collection of statistical algorithms that adhere to the definition ofdifferential privacy.
It can be used to build applications of privacy-preserving computations, using a number of different models of privacy.
OpenDP is implemented in Rust, with bindings for easy use from Python and R.The architecture of the OpenDP Library is based on a conceptual framework for expressing privacy-aware computations.
This framework is described in the paperA Programming Framework for OpenDP.The OpenDP Library is part of the largerOpenDP Project, a community effort to build trustworthy,
open source software tools for analysis of private data.
(For simplicity in these docs, when we refer to “OpenDP,” we mean just the library, not the entire project.)StatusOpenDP is under development, and we expect torelease new versionsfrequently,
incorporating feedback and code contributions from the OpenDP Community.
It's a work in progress, but it can already be used to build some applications and to prototype contributions that will expand its functionality.
We welcome you to try it and look forward to feedback on the library! However, please be aware of the following limitations:OpenDP, like all real-world software, has both known and unknown issues.
If you intend to use OpenDP for a privacy-critical application, you should evaluate the impact of these issues on your use case.More details can be found in theLimitations section of the User Guide.InstallationInstall OpenDP for Python withpip(thepackage installer for Python):$ pip install opendpInstall OpenDP for R from an R session:install.packages("opendp", repos = "https://opendp.r-universe.dev")More information can be found in theGetting Started section of the User Guide.DocumentationThe full documentation for OpenDP is located athttps://docs.opendp.org. Here are some helpful entry points:User GuidePython API DocsContributor GuideGetting HelpIf you're having problems using OpenDP, or want to submit feedback, please reach out! Here are some ways to contact us:Ask questions on ourdiscussions forumOpen issues on ourissue trackerJoin ourSlackSend general queries toinfo@opendp.orgReach us on Twitter at@opendp_orgContributingOpenDP is a community effort, and we welcome your contributions to its development!
If you'd like to participate, please contact us! We also have acontribution process section in the Contributor Guide.
Package: opendp-logger
OpenDP LoggerThe OpenDP logger makes it possible to serialize and deserialize OpenDP Measurements/Transformations to/from JSON.SerializeEnable logging (globally) before you build your transformations and/or measurements:fromopendp_loggingimportenable_loggingenable_logging()Once this is enabled, Transformations/Measurements have a method.to_json()that returns a JSON string.DeserializeDeserialize a JSON string into a Transformation/Measurement by invokingopendp_logger.make_load_json.Examplefromopendp_loggerimportenable_loggingfromopendp.modimportenable_featuresenable_logging()enable_features("contrib")importopendp.transformationsastranspreprocessor=(# load data into a dataframe where columns are of type Vec<str>trans.make_split_dataframe(separator=",",col_names=["hello","world"])>># select a column of the dataframetrans.make_select_column(key="income",TOA=str))# serialize the chain to jsonjson_obj=preprocessor.to_json()print("json:",json_obj)fromopendp_loggerimportmake_load_json# reconstruct the obj from the json stringtest=make_load_json(json_obj)
Package: opendp-pytorch
OpenDP-PyTorchOpenDP-PyTorch is an extension to theOpenDPlibrary for
differentially private training of pytorch neural networks.OpenDP-PyTorch is part of the largerOpenDP Project, a community effort to build trustworthy,
open source software tools for analysis of private data.StatusOpenDP-PyTorch is under development, and we expect torelease new versionsfrequently,
incorporating feedback and code contributions from the OpenDP Community.
This library is not ready to build privacy-sensitive applications.OpenDP, like all real-world software, has both known and unknown issues.
If you intend to use OpenDP for a privacy-critical application, you should evaluate the impact of these issues on your use case.More details can be found in theLimitations section of the User Guide.InstallationThe easiest way to install OpenDP-PyTorch is usingpip(thepackage installer for Python):$ pip install opendp-pytorchMore information can be found in theGetting Started section of the User Guide.DocumentationDocumentation is not yet available for the PyTorch extension.Getting HelpIf you're having problems using OpenDP, or want to submit feedback, please reach out! Here are some ways to contact us:Ask questions on ourdiscussions forumOpen issues on ourissue trackerSend general queries toinfo@opendp.orgReach us on Twitter at@opendp_orgContributingOpenDP is a community effort, and we welcome your contributions to its development!
If you'd like to participate, please see theContributing section of the Developer Guide
Package: opendp-smartnoise
SmartNoise: Tools for Differential PrivacyThe SmartNoise tools allow researchers and analysts to:Use SQL dialect to create differentially private results over tabular data storesPerform privacy algorithm stochastic testing against differential privacy modulesThe SmartNoise system is currently aimed at scenarios where the researcher is trusted by the data owner.  Future releases will focus on hardened scenarios where the researcher or analyst is untrusted.
Package: opendp-whitenoise
WhiteNoise: Tools for Differential PrivacyThe WhiteNoise tools allow researchers and analysts to:Use SQL dialect to ceate differentially private results over tabular data storesHost a service to compose queries from heterogeneous differential privacy modules (including non-SQL) against shared privacy budgetPerform black-box stochastic testing against differential privacy modulesThe WhiteNoise system is currently aimed at scenarios where the researcher is trusted by the data owner.  Future releases will focus on hardened scenarios where the researcher or analyst is untrusted.
Package: open-dread-rando
Open Dread RandoOpen Source randomizer patcher for Metroid Dread. Intended for use inRandovania.
Currently supports patching item pickups, starting items, and elevator/shuttle/teleportal destinations.Installationpip install open-dread-randoUsageYou will need to provide JSON data matching theJSON schemain order to successfully patch the game.The patcher expects a path to an extracted romfs directory of Metroid Dread 1.0.0 or 2.1.0 as well as the desired output directory.
Output files are in a format compatible with either Atmosphere or Ryujinx, depending on the settings provided.With a JSON file:python -m open_dread_rando --input-path path/to/dread/romfs --output-path path/to/the/output-mod --input-json path/to/patcher-config.jsonGame VersionsOnly versions 1.0.0 and the latest version are supported long term. Other versions might be compatible at any given point,
but new releases are free to remove that.Currently, the following versions are supported:1.0.02.1.0
Package: opendrive2lanelet
OpenDRIVE 2 Lanelet - ConverterThis tool is deprecated! Please use our newCommonRoad Scenario Designer!We provide the code for an OpenDRIVE (www.opendrive.org) to lanelets (www.mrt.kit.edu/software/liblanelet) converter, which has been introduced in ourpaper: M. Althoff, S. Urban, and M. Koschi, "Automatic Conversion of Road Networks from OpenDRIVE to Lanelets," in Proc. of the IEEE International Conference on Service Operations and Logistics, and Informatics, 2018.
Additionally, we provide a converter from CommonRoad lanelets to OSM lanelets and vice versa.InstallationInstalling from sourcegitclonehttps://gitlab.lrz.de/cps/opendrive2lanelet.git
pythonsetup.pyinstallPublic source (only released versions):https://gitlab.lrz.de/tum-cps/opendrive2lanelet.gitUsing pip:pipinstallopendrive2laneletExample OpenDRIVE FilesDownload example files from:http://opendrive.org/download.htmlUsageUsing our provided GUIOpening OpenDRIVE to CommonRoad converter GUI from the command line:opendrive2lanelet-guiConverting files using the command lineConverting a file from OpenDRIVE to CommonRoad with the command line:opendrive2lanelet-convertinput_file.xodr-ooutput_file.xmlConverting a file from OSM lanelets to CommonRoad lanelets with the command line (for description of input parameters see documentation):osm-convertinputfile.xml--reverse-ooutputfile.osm--adjencies--proj"+proj=etmerc +lat_0=38 +lon_0=125 +ellps=bessel"For the conversion of CommonRoad lanelets to OSM lanelets change the input and output file accordingly.
Visualizing the results of the conversion to CommonRoad:opendrive2lanelet-visualizeinput-file.xmlDocumentationThe documentation is published on theCommonRoadhomepage.To generate the documentation from source, first install the necessary dependencies with pip:pipinstall-rdocs_requirements.txtThen you can runcddocs&&makehtmlfor example.Known ProblemsWhen trying to use the gui.py under Wayland, the following error occurs:This application failed to start because it could not find or load the Qt platform plugin "wayland" in "".
Available platform plugins are: eglfs, linuxfb, minimal, minimalegl, offscreen, vnc, xcb.
Reinstalling the application may fix this problem.Set the platform toxcbusing this command:export QT_QPA_PLATFORM="xcb"AuthorsSebastian Maierhofer (current maintainer)Benjamin OrthenStefan Urban