Saravanan666/pypi-org huggingface dataset:
Package: harfang
Harfang for Python3D real time visualization frameworkHarfang is a 3D real time visualization framework for the industry, the education and for scientists. It grants developpers the ability to create applications ranging from data visualization to games.Seehttps://www.harfang3d.com/licensefor licensing terms.Quickstart, command line method (Windows/Linux)Clone the tutorials’ repository:git clonehttps://github.com/harfang3d/tutorials-hg2.gitEnter the repository:cdtutorials-hg2Compile the tutorial resources:python3-mharfang.bin assetc resourcesAfter the compilation process finishes, you should see aresources_compiledfolder next to the resources folder.Run the tutorials:D:\tutorials-hg2>python3draw_lines.pyQuickstart, drag & drop method (Windows)Download the tutorialshttps://github.com/harfang3d/tutorials-hg2and unzip them to your computer (eg.d:/tutorials-hg2).To compile the tutorial resources, downloadassetcfor your platform:https://dev.harfang3d.com/releases/Drag and drop the tutorial resources folder on the`assetcexecutable -OR- executeassetcpassing it the path to the tutorial resources folder (eg.assetc d:/tutorials-hg2/resources).After the compilation process finishes, you should see aresources_compiledfolder next to the resources folder. You can now execute the tutorials from the folder you unzipped them to.D:\tutorials-hg2>pythondraw_lines.pyAlternatively you can open the tutorial folder and run the provided debug targets usingVisual Studio CodeScreenshotsThe following screenshots were captured on a 2060RTX in 1080P running at 60FPS, GI is performed using screen space raytracing and does not require RTX capable hardware.The NVIDIA USD Attic using the AAA renderer(Courtesy of NVIDIA)Marine Melodies(interactive musical experience)Cyber City(CyberPunk City, CyberPunk Girl and Robot R32 by art-equilibrium, ILranch and ZeroArt3d)Sun Temple(Sun Temple, courtesy of the Open Research Content Archive)Cafe Exterior(Bistro, courtesy of the Open Research Content Archive)Sponza Atrium(Sponza Atrium GLTF, courtesy of Crytek/Themaister)FeaturesScene APINode & component basedPerformance orientedRendering pipelineLow-spec PBR rendering pipelineHigh-spec ‘AAA’ rendering pipeline (screen space GI & reflection)Support of user pipeline shadersVR APIVR support via OpenVR/SteamVR with Eye trackingCompatible with the HTC Vive/Vive Pro, Valve Index, Lenovo Explorer, Oculus Rift SPhysics APIRigid bodies, collisions, mechanical constraintsRay castingFast & accurateAudio APIPlay/stream WAV/OGG formats3D audio spatializationMore information onhttps://www.harfang3d.com/
Package: harf-serde
A library of classes forHARdata usingpyserde.The library is focused on providing a completeF-AlgebraforHARdata and associatedmorphismsbut also containscanonical typesfor basic usages.F-Algebra classesThere is one generic-ish class for everyHARtype and are prefixed withF.
All nestedHARdata has been replaced with type parameters.
Some types have no nestedHARdata and so have no generic parameters but are included for completeness.
All types can be imported fromharf_serde.harfor directly fromharf_serde.There is one edge case for post data.
Since some of the parameters defined in the spec are mutually exclusive there are two classes for post data.PostDataParamFforParamFbased post data, andPostDataTextFfor text based post data.
There is a union typePostDataFcombining the two for type hints but it cannot be constructed.All of these types are Functory, by providing anmapfunction that takes a function for each of the generic parameters.
For example, see theResponseFclass below.classResponseF(Generic[A,B,C]):...statusText:strhttpVersion:strcookies:List[A]headers:List[B]content:C...defnmap(self:"ResponseF[A, B, C]",f:Func[A,W],g:Func[B,X],h:Func[C,Y],)->"ResponseF[W, X, Y]":returnreplace(self,# type: ignore[arg-type]cookies=list(map(f,self.cookies)),headers=list(map(g,self.headers)),content=h(self.content),)MorphismsCurrently there are just 2 helper morphisms.harf_catais a general catamorphsim, andharfwhich helps you build an algebra forharf_cataby taking explicit functions for eachHARtype.
All functions can be imported fromharf_serde.morphismor directly fromharf_serde.For example, if you wanted to count the number of times the string"key"is in headers you could do the followingharf(default=0,header=lambdah:h.count("key"),request=lambdar:sum(r.headers),response=lambdar:sum(r.headers),entry=lambdae:e.request+e.response,log=lambdal:sum(l.entries),)(har_data)Canonical/Basic typesFor simple usages there are also type aliases for the canonicalHARstructure.
I.E.Response = ResponseF[Cookie, Header, Content].
All types can be imported fromharf_serde.haror directly fromharf_serde.
Package: hargassner
Hargassner APIThis Python module enables you to query information about your Hargassner devices from their Web portal.Your device must be connected to the Web portal of Hargassner (https://web.hargassner.at). To use the module, you will
need access to the following information:InformationDescriptionUsernameThis is your username that you use to log into the web portal.PasswordThis is your password that you use to log into the web portal.Client SecretThe client secret is a cryptographic key derived from your login information. It can be found in the developer tools from your browser while navigating the web portal. Look for a POST request "login" and inspect the JSON information that is sent to the Hargassner server. It will contain your username, password and the client secret.Installation IDThis is the ID of your installation. You can find it in the address bar of your browser when you navigate through the web portal (https://web.hargassner.at/installations/-/info).Usage/Examplesfromhargassner.clientimportHargassnerAPI# MAINif__name__=="__main__":h=HargassnerAPI()h.username='<your Hargassner username>'h.password='<your Hargassner password>'h.client_secret='<your client secret from Hargassner web'h.installation='<your Hargassner installation ID>'h.authdat='auth.dat'h.debug=Truej=h.query_api()if'data'inj:forwinj['data']:ifw['widget']=='EVENTS':foreinw['values']:print('Ereignis: '+e['created_at']+" -> "+e['text'])elifw['widget']=='HEATER':print('Name:'+w['values']['name'])print('State:'+w['values']['state'])print('Abgastemperatur: '+str(w['values']['smoke_temperature']))print('Kesseltemperatur: '+str(w['values']['heater_temperature_current']))print('Aussentemperatur: '+str(w['values']['outdoor_temperature']))elifw['widget']=='BUFFER':print('Puffer Füllgrad: '+str(w['values']['buffer_charge']))print('Puffer Temperatur Oben: '+str(w['values']['buffer_temperature_top']))print('Puffer Temperatur Mitte: '+str(w['values']['buffer_temperature_center']))print('Puffer Temperatur Unten: '+str(w['values']['buffer_temperature_bottom']))else:print("PROBLEM")
Package: hargutil
hargutilA tool for dealing args.
ReadChinese versionif possible.请尽可能看中文版本。InstallThe package can be installed through pip (this is the recommended method)pip install hargsOr downloading the release fromPypior GithubUsageNewFirst, create aHargsclass:hargs = Hargs(sys.argv)Hargs.add()To add some switches, use functionHargs.add(short:str, long:str, map: str)Args instructions:shortlike-o=output.txtlonglike--input-file=input.txtmapFor convincing of using. Depend by yourself.Example:hargs.add('v','version','v')\.add('f','file','f')\.add('o','output','o')\.add('','force','ff')\#   ... ...It's worth noticing thatshortandlongcan't be''at the same time.mapcan't be''Hargs.to_dict()Thenhargscan be transformed intodict:hargs = hargs.to_dict()Example:./hargs.py -v -o=output.txt --forcewill turn into{'v':'','f':None,'o':'output.txt','ff':''}
Package: harkness
harknessA python library for generating VR-friendly visualization files from pandas dataframes.Try it in Binder:How to Use:Identify an appropriate dataframe.You'll want to make sure that your data makes sense in 3D, and select or add a column that makes sense as a colormap. The colormap column can be categorical or numeric.Generate .ply file.Runcreate_ply()on your dataframe, identifying the columns X, Y, Z, and C (for color).Visualize on desktop.You can use a program likeAutodesk MeshMixerorCloud Compare.Visualize in VR.You can open the resulting file in a VR viewer likeVRifier. Any headset that works withSteamVRshould work. I use aValve Index.Notes and AcknowledgmentsNamed for Ruth Harkness, who with Quentin Young and Gerald Russell brought the first live giant panda cub to the United States. (For more on this, read her memoir,The Lady and the Panda.) Name chosen because (1) "harkness" did not appear for related topics on a search of pypi, and (2) it's a library for displaying pandas. Some assistance by Google Bard.If you use this project in your work, please cite it using the Zenodo DOI:
Package: harlequin_bigquery
harlequin-bigqueryThis is a BigQuery adapter forHarlequin, a SQL IDE for the terminal.This adapter will use Application Default Credentials to authenticate with BigQuery and run queries.ConfigurationThis adapter supports the following options:project: The ID of the Google Cloud project to run Harlequin in. Defaults to whatever it can infer from the user's environment, i.e.gcloud config list project.location: Thelocationused to run the catalog queries, whichmust be region-qualified. Defaults toUS.Required permissionsThe user will need the permission to query bothINFORMATION_SCHEMA.TABLESandINFORMATION_SCHEMA.COLUMNSto load the data catalog.To query these views, you need the following Identity and Access Management (IAM) permissions:bigquery.tables.getbigquery.tables.listbigquery.routines.getbigquery.routines.listEach of the following predefined IAM roles includes the necessary permissions:roles/bigquery.adminroles/bigquery.dataViewerroles/bigquery.metadataViewerFor more information about BigQuery permissions, seeAccess control with IAM.
Package: harlequin-databricks
harlequin-databricksAHarlequinadapter for Databricks. Supports connecting to Databricks SQL
warehouses or Databricks Runtime (DBR) interactive clusters.Installationharlequin-databricksdepends onharlequin, so installing this package will also install Harlequin.Using pipTo install this adapter into an activated virtual environment:pipinstallharlequin-databricksUsing poetrypoetryaddharlequin-databricksUsing pipxIf you do not already have Harlequin installed:pipxinstallharlequin-databricksIf you would like to add the Databricks adapter to an existing Harlequin installation:pipxinjectharlequinharlequin-databricksAs an ExtraAlternatively, you can install Harlequin with thedatabricksextra:pipinstallharlequin[databricks]poetryaddharlequin[databricks]pipxinstallharlequin[databricks]Usage and ConfigurationFor a minimum connection you are going to need:server-hostnamehttp-pathaccess-tokenharlequin-adatabricks--server-hostnamemy_databricks.cloud.databricks.com--http-path/sql/1.0/endpoints/1234567890abcdef--access-tokendabpi***Authentication is also possible using a username and password (known as basic authentication):harlequin-adatabricks--server-hostnamemy_databricks.cloud.databricks.com--http-path/sql/1.0/endpoints/1234567890abcdef--usernamemy_user--passwordmy_passOr by usingOAuth user-to-machine (U2M) authentication:harlequin-adatabricks--server-hostnamemy_databricks.cloud.databricks.com--http-path/sql/1.0/endpoints/1234567890abcdef--auth-typedatabricks-oauthFor more details on command line options, run:harlequin--helpFor more information, see theharlequin-databricks Docs.Using Unity Catalog and want fast Data Catalog indexing?Supply the--skip-legacy-indexingcommand line flag if you do not care about legacy metastores
(e.g.hive_metastore) being indexed in Harlequin's Data Catalog pane.This flag will skip indexing of old non-Unity Catalog metastores (i.e. they won't appear in the
Data Catalog pane with this flag).Because of the way legacy Databricks metastores works, a separate SQL query is required to fetch
the metadata of each table in a legacy metastore. This means indexing them for Harlequin's Data Catalog pane is slow.Databricks's Unity Catalog upgrade broughtInformation Schema,
which allows harlequin-databricks to fetch metadata for all Unity Catalog assets with only two SQL queries.So if your Databricks instance is running Unity Catalog, and you no longer care about the legacy
metastores, setting the--skip-legacy-indexingCLI flag is recommended as it will mean
much faster indexing & refreshing of the assets in the Data Catalog pane.Issues, Contributions and Feature RequestsPlease report bugs/issues with this adapter via the GitHubissuespage. You are welcome to
attempt fixes yourself by forking this repo then opening anPR.For feature suggestions, please post in thediscussions.Special thanks to...Ted Conbeer,Josh Temple&Tyler Hillery.
Package: harlequin-mysql
harlequin-mysqlThis repo provides the Harlequin adapter for MySQL.Installationharlequin-mysqldepends onharlequin, so installing this package will also install Harlequin.Using pipTo install this adapter into an activated virtual environment:pipinstallharlequin-mysqlUsing poetrypoetryaddharlequin-mysqlUsing pipxIf you do not already have Harlequin installed:pipinstallharlequin-mysqlIf you would like to add the Postgres adapter to an existing Harlequin installation:pipxinjectharlequinharlequin-mysqlAs an ExtraAlternatively, you can install Harlequin with themysqlextra:pipinstallharlequin[mysql]poetryaddharlequin[mysql]pipxinstallharlequin[mysql]Usage and ConfigurationYou can open Harlequin with the MySQL adapter by selecting it with the-aoption and passing connection parameters as CLI options:harlequin-amysql-hlocalhost-p3306-Uroot--passwordexample--databasedevThe MySQL adapter does not accept a connection string or DSN.Many more options are available; to see the full list, run:harlequin--helpFor more information, see theHarlequin Docs.
Package: harlequin-odbc
harlequin-odbcThis repo provides the ODBC adapter for Harlequin.Installationharlequin-odbcdepends onharlequin, so installing this package will also install Harlequin.Pre-requisitesYou will need an ODBC driver manager installed on your OS. Windows has one built-in, but for Unix-based OSes, you will need to download and install one before installingharlequin-odbc. You can install unixODBC withbrew install unixodbcorsudo apt install unixodbc. See thepyodbc docsfor more info.Additionally, you will need to install the ODBC driver for your specific database (e.g.,ODBC Driver 18 for SQL Serverfor MS SQL Server). For more information, see the docs for your specific database.Using pipTo install this adapter into an activated virtual environment:pipinstallharlequin-odbcUsing poetrypoetryaddharlequin-odbcUsing pipxIf you do not already have Harlequin installed:pipinstallharlequin-odbcIf you would like to add the ODBC adapter to an existing Harlequin installation:pipxinjectharlequinharlequin-odbcAs an ExtraAlternatively, you can install Harlequin with theodbcextra:pipinstallharlequin[odbc]poetryaddharlequin[odbc]pipxinstallharlequin[odbc]Usage and ConfigurationYou can open Harlequin with the ODBC adapter by selecting it with the-aoption and passing an ODBC connection string:harlequin-aodbc'Driver={ODBC Driver 18 for SQL Server};Server=tcp:harlequin-example.database.windows.net,1433;Database=dev;Uid=harlequin;Pwd=my_secret;Encrypt=yes;TrustServerCertificate=no;Connection Timeout=30;'The ODBC adapter does not accept other options.For more information, see theHarlequin Docs.
Package: harlequin-postgres
harlequin-postgresThis repo provides the Harlequin adapter for Postgres.Installationharlequin-postgresdepends onharlequin, so installing this package will also install Harlequin.Using pipTo install this adapter into an activated virtual environment:pipinstallharlequin-postgresUsing poetrypoetryaddharlequin-postgresUsing pipxIf you do not already have Harlequin installed:pipinstallharlequin-postgresIf you would like to add the Postgres adapter to an existing Harlequin installation:pipxinjectharlequinharlequin-postgresAs an ExtraAlternatively, you can install Harlequin with thepostgresextra:pipinstallharlequin[postgres]poetryaddharlequin[postgres]pipxinstallharlequin[postgres]Usage and ConfigurationYou can open Harlequin with the Postgres adapter by selecting it with the-aoption and passing aPosgres DSN:harlequin-apostgres"postgres://my-user:my-pass@localhost:5432/my-database"You can also pass all or parts of the connection string as separate options. The following is equivalent to the above DSN:harlequin-apostgres-hlocalhost-p5432-Umy-user--passwordmy-pass-dmy-databaseMany more options are available; to see the full list, run:harlequin--helpFor more information, see theHarlequin Docs.
Package: harlequin_trino
harlequin-trinoThis repo provides the Harlequin adapter for Trino.Installationharlequin-trinodepends onharlequin, so installing this package will also install Harlequin.Using pipTo install this adapter into an activated virtual environment:pipinstallharlequin-trinoUsing poetrypoetryaddharlequin-trinoUsing pipxIf you do not already have Harlequin installed:pipinstallharlequin-trinoIf you would like to add the Trino adapter to an existing Harlequin installation:pipxinjectharlequinharlequin-trinoAs an ExtraAlternatively, you can install Harlequin with thetrinoextra:pipinstallharlequin[trino]poetryaddharlequin[trino]pipxinstallharlequin[trino]Usage and ConfigurationFor a minimum connection you are going to need:hostportuserharlequin-atrino-hlocalhost-p8080-Umy_userIf your trino instance requires a password you can set the--require_authflag to password and use the--passwordflag for your passwordharlequin-atrino-hlocalhost-p8080-Umy_user--passwordmy-pass--require_authpasswordMany more options are available; to see the full list, run:harlequin--helpFor more information, see theHarlequin Docs.
Package: harlow
harlowAdaptive surrogate modelling.f_target(x) ~ f_surrogate(x)Harlow is an Adaptive Surrogate Modeling package, in Python.
The package offers a wide range of GPU-trainable Surrogate Models for single input-output and multi input-output pairs. Additionaly a series of Adaptive Samplers is implemented that work with multivariate data providing real-time web-logging. The package offers an intergration and benchmark test suite with numerous test functions and a real case study along with visualization functionality.DISCLAIMER: This repository is in development. There's no guarantee in terms of code quality or output.On using the repositoryInstall dependencies and the code from this repo:pip install -e .Install with additional dependencies for building documentation:pip install -e .[docs]To build the documentation locally:sphinx-build -b html docs/source docs/buildTo view the documentation opendocs/build/index.htmlAll code within this repository is expected to be run with the
working directory set as the root directory of the repository.
Package: harm-analysis
IntroductionThe harmonic analysis function uses an FFT to estimate the following parameters from a signal containing a tone:THD and THD+NFundamental power and frequency locationNoise powerSNR, SINADDC levelTotal integrated noise (everything except DC and the fundamental)The full documentation is hosted on ReadTheDocs:Harmonic Analysis.InstallationThe harm_analysis package is available via PIP install:python3-mvenvpyenvsourcepyenv/bin/activatepipinstallharm_analysisAfter installing the package, the harm_analysis function should be available via import:fromharm_analysisimportharm_analysisDocumentation on how to use the function can be foundhere.Command line interfaceInstalling the package also installs a command line interface, that allows the user to
run the function for text files with time domain data:The command isharm_analysis:harm_analysis --helpOutput:Usage: harm_analysis [OPTIONS] FILENAME

  Runs the harm_analysis function for a file containing time domain data

Options:
  --fs FLOAT      Sampling frequency.
  --plot          Plot the power spectrum of the data
  --sep TEXT      Separator between items.
  --sfactor TEXT  Scaling factor. The data will be multiplied by this number,
                  before the function is called. Examples: 1/8, 5, etc
  --help          Show this message and exit.
Package: harmon
Harmon Chess EngineHarmon Chess Engine is a cutting-edge chess engine that leverages the power of transformer-based machine learning models to revolutionize the world of computer chess. Unlike traditional chess engines, Harmon is designed to understand and exploit human mistakes, making it a formidable opponent for chess players of all levels.FeaturesTransformer-Powered Chess Engine: Harmon uses state-of-the-art transformer models to predict human moves, allowing it to adapt its strategy based on the tendencies and mistakes commonly made by human players.Human Mistake Exploitation: Harmon's unique approach allows it to identify and capitalize on common human errors, giving it a strategic advantage in games.Game Analysis: Analyze your chess games to gain insights into your playing style and improve your skills. Harmon can provide detailed game summaries, identify key turning points, and suggest areas for improvement.Elo Prediction: Harmon can estimate the Elo rating of chess players based on their game performance, helping players gauge their progress and understand their strengths and weaknesses.Game Outcome Prediction: Harmon can predict the likely outcome of a chess game, providing players with valuable insights into the flow and dynamics of the match.Getting StartedInstallationpipinstallharmonUsageTrain the model with your preferred dataset, such as PGN files from lichess.Configure Harmon's settings and model parameters to suit your needs.Run Harmon to analyze games, predict outcomes, or play chess against it.Example UsageTrain the modelpython-mharmon.trainGeneratepython-mharmon.generateContributingWe welcome contributions from the open-source community! If you're interested in improving Harmon Chess Engine or adding new features, please see ourContribution Guidelinesfor details on how to get started.LicenseThis project is licensed under the MIT License - see theLICENSEfile for details.AcknowledgmentsThe Harmon Chess Engine project is inspired by the fascinating world of chess and the advancements in machine learning and artificial intelligence.Special thanks to the contributors and developers who have made this project possible.Disclaimer:Harmon Chess Engine is a research project and may not be suitable for professional or competitive chess use. Use it for educational and recreational purposes, and have fun exploring the world of chess!
Package: harmonator
HarmonatorThe Harmontown downloaderRunning this application will get you up to date with the latest episodes of Harmontown. Filename
formatting is automatic and complies to the following format:harmontown - S01E01 - Episode title.mp3InstallationHarmonator can be installed with pip:pip install harmonatorUsageHarmonator is used from the command line and cen be used to download either a single episode,
episodes after a date, or all missing episodes.Download a single episode by episode number:harmonate.py --episode=100Authors:Created by Chris Read