Saravanan666/pypi-org huggingface dataset:
Package: dg-ai-platform
Caldron AI platformversion 0.1.13System ArchitectureHow to run hello world demo1. pip install dg-ai-platform.2. Login to Caldron AI platform website.3. You will see a HelloWorld app with PID and public key.4. Paste following code to a blank python file and change #pid# to your demo PID.5. run this python file.fromdg_ai_platform.exampleimportHelloWorld
fromdg_ai_platform.dg_platformimportCaldronAIca=CaldronAI(HelloWorld,pid="xxxx",public_key="xxxx")ca.run()6. Create your task in HelloWorld task page.7. Your local python process will fetch a task and process it.8. When the task finished, result will show on the pageRun your own App1. Create a app then setup inputs and outputs on website.2. Create yourself class must be implementing ITaskProcess(see below code).fromdg_ai_platform.dg_platformimportITaskProcess

classCustomTask(ITaskProcess):def__init__(self):super().__init__()definference(self,input_list,output_list,options=None):#your logic3.You should be let your logic into function inference(self, input_list, output_list, options=None)param input_list: all inputs local path in this array. You should be load your all inputs then put it to your model.param output_list: all outputs local path in this array. You should be save your outputs to these outputs path.param options: This is a json data.4. Run your app with CustomTaskfromyour_python_fileimportCustomTask
fromdg_ai_platform.dg_platformimportCaldronAIca=CaldronAI(CustomTask,pid="xxxx",public_key="xxxx")ca.run()5. Create your custom task on website.6. You will see a result on result page.
Package: dgarbsutils
UtilsDescriptionI found myself using the same code over and over across projects so I created a package to import my own code faster.InstallpipinstalldgarbsutilsUsagefromdgarbsutilsimportutils,awsUtilsBuildpython-mbuildUploadpython3-mtwineupload--repositorypypidist/*
python3-mtwineupload--repositorynexusdist/*
Package: dgb
Dynamic Graph ProcessingProvidesA data-loader to download a suite of dynamic graph datasets.An evaluator for the suit of dynamic graph datasetsTools to benchmark graph models, example code snippets, tutorials, etc. (ongoing...)To installuse pip for Python, make sure version python version is 3.6+>>> pip install dgbCode examplesCode snippet to import the module::import dgbCode snippets to download a dataset::enron = dgb.TemporalDataSets(data_name="enron")
enron_dict = enron.process()
train = enron_dict["train"]
test  = enron_dict["test"]
val   = enron_dict["validation"]to print all possible datasets::data_list = dgb.list_all()
for data_name in data_list:
print(data_name)to download all possible datasets that have not been downloaded yet::dgb.download_all()to force redownload all datasets::dgb.force_download_all()to skip download prompts and dataset statistics when processing::dgb.TemporalDataSets(data_name="enron", skip_download_prompt=True, data_set_statistics=False)LicenseMIT License
Package: dgbg
Docking Grid Box Generator (DGBG)Python module to automatically generate a grid box around a ligand present in a pdb file.PreviewTable of contentsDemo-PreviewTable of contentsInstallationUsageDevelopmentAcknowledgementsLicenseInstallation(Back to top)To use this project, pip install the module on your device using the command below:pip install dgbgUsage(Back to top)fromdgbgimportdgbgbox=dgbg.binding_box('ex_file_path.pdb')ligand_data=box.get_ligand_data('ligand_ID','chain_ID')centroid,box_size=box.create_box(spacing=0.375,padding=np.array([0,0,0]))# Visualize using the following commands in Jupyter Notebook.result=box.show_result(centroid,box_size)resultExamples(Back to top)General example use case.Adjusting grid box after generation.Integration with DeepChem and its docking functionality.Integration with AutoDock Vina.Multiple identical ligands on the same protein chain.Development(Back to top)Git Clone Repositorygit clone git@github.com:ujamshed/dgbg.gitCreate Virtual Environmentpython3  -m venv .venvActivate Virtual Environmentsource .venv/bin/activateInstall Project Dependenciespip install -r requirements.txtAcknowledgementsThis software uses the following open source packages:NumPyPandasMDTrajnglviewLicense(Back to top)MIT
Package: dgbowl-schemas
dgbowl-schemasSchemas and validators for thedgbowlsuite of tools.Currently implemented schemas are:Recipevalidator fordgpost, versions{2.1, 1.0}DataSchemavalidator foryadg, versions{5.0, 4.2, 4.1, 4.0}Payloadvalidator fortomato,  at version{0.2, 0.1}
Package: dg-chatgpt
DevGroundChatGPT API wrapperThis is a wrapper for theChatGPT APIbyDevGround.Modelgpt-3.5-turboLicenseIt is licensed under the MIT license.LanguageWritten in Python 3.11Requirementsrequires environment variables:OPENAI_API_KEYInstallationinstall the package with pip:pipinstalldg_chatgpt_apiUsagefromdg_chatgpt_apiimportChatGPTAPIasapiapi.reconnect()api.add_user("user_id","username")api.add_conversation("user_id")message={"role":"user","content":"message"}response=api.get_response("user_id",message)
Package: dg-commons
dg-commons (Driving Games common tools)This package contains common tools that ease the development and quick prototyping of autonomous vehicles with a focus
on multi-agent planning and decision-making.Few HighlightsMulti-agent simulationCollision resolutionProbabilistic sensorsSimulatorThe simulator builds on a clear distinction between the concept of an "agent" and its corresponding physical "model".Anagentreceivesobservationsand returnscommandsto the simulator.
Amodelinstead represents the physical instance of the agent in the simulation.
The receivedcommandsfrom the agent are used to update its state according to the multi-agent simulation.Collision resolutionCollision detection is performed at each step of the physical simulation, typically higher rate than the agents' updates.
The framework provides also a basiccollision resolutionsubroutine for the models.
This is based on theimpulse responsetechnique among rigid bodies used in game engines.
More references are provided in the corresponding location in the docs.Commonroad integrationMost of the scenario tools natively integrate with theCommonroad project.
Few scenarios used mainly for testing are available in thescenariosfolder.
For more scenarios consider cloning theCommonroad scenarios repository.
For internal use (private) consider alsodg-scenarios.InstallationThe package is distributed on PyPI. You can simply install it viapipinstalldg-commonsto install also the developer tools usepip install dg-commons["all"].Pre-commit hook (for developers)Install pre-commit withpipinstallpre-commit
pre-commitinstallRun pre-commit withpre-commitrun--all-filesCompatibilityFrom version 0.30 onwards the package is tested against python 3.9, 3.10, 3.11.
It might work also for other versions, but it is not tested.PublicationsThe tools contained in this package have contributed to the following publications:Posetal Games: Efficiency, Existence, and Refinement of Equilibria in Games with Prioritized Metrics-A. Zanardi*, G. Zardini*, S. Srinivasan, S. Bolognani, A. Censi, F. Dörfler, E. Frazzoli- IEEE Robotics and Automation Letters, 2022Factorization of Dynamic Games over Spatio-Temporal Resources-A. Zanardi, S. Bolognani, A. Censi, F. Dörfler, E. Frazzoli- Proceedings of the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022Task-driven Modular Co-design of Vehicle Control Systems-G. Zardini, Z. Suter, A. Censi, E. Frazzoli- Proceedings of the 61th IEEE Conference on Decision and Control (CDC), 2022Factorization of Multi-Agent Sampling-Based Motion Planning-A. Zanardi, P. Zullo, A. Censi, E. Frazzoli- Proceedings of the 62nd IEEE Conference on Decision and Control (CDC), 2023A Counterfactual Safety Margin Perspective on the Scoring of Autonomous Vehicles' Riskiness-A. Zanardi, A. Censi, M. Atzei, L. Di Lillo, E. Frazzoli- ArXiv preprint, 2023Use and contributionsIf you find some of the tools provided in this repository useful, consider citing it in your research via the provided github citation.
If you want to contribute with new functionalities or need help in using the provided tools consider please feel free to open an issue.
Package: dgec2
# Package of Measurement of Digital Economy### Intro> Based on the analysis of newspaper text and network dynamics, this index can extract the implied information in newspapers to replace traditional economic data to construct the measurement of digital economy. The advantage of this measure is that it includes both direct hard information and indirect soft factors. In addition, it can dynamically simulate influence indicators to provide suggestions for accurate decision-making of the government###function`db_out():db_out `Get all the data about digital economy,you will get a DataFrame data.` db_year(data,city) `Output digital economy index for all regions by year,and you will alos get a DataFrame data.` db_city(data,city) `Output digital economy index for time by city,and you will alos get a DataFrame data.` tolist(data) `Transform data from DataFrame to a list
Package: dgenies
D-GeniesDot plots are widely used to quickly compare sequence sets. They provide a synthetic similarity overview, highlighting repetitions, breaks and inversions. Different tools have been developed to easily generated genomic alignment dot plots, but they are often limited in the input sequence size. D-GENIES is a standalone and WEB application performing large genome alignments usingMinimap2orMashMap v2.0software packages and generating interactive dot plots. It enables users to sort query sequences along the reference, zoom in the plot and download several image, alignment or sequence files. D-GENIES is an easy to install open source software package (GPL) developed in Python and JavaScript.How to use?You can use thedemo instance hosted at genotoul.Or you can deploy your own instance with the help ofthe installation documentation.DocumentationFull documentation, including code API, is availablehere.How to cite?Cabanettes F, Klopp C. (2018) D-GENIES: dot plot large genomes in an interactive, efficient and simple way. PeerJ 6:e4958https://doi.org/10.7717/peerj.4958CreditsD-Genies is developed by theBioinfo teamof theGenotoul platform(INRAE).
Package: dgenome
dgenomeDgenome is a Django app which handle the genome coordinatesQuick startAdd “dgenome” to your INSTALLED_APPS setting like this:INSTALLED_APPS = [
    ...
    'dgenome',
]Include the dgenome URLconf in your project urls.py like this:url(r'^dgenome/', include((dgenome.urls, 'dgenome'), namespace='dgenome')),Runpython manage.py makemigrations dgenometo create the dgenome models.Runpython manage.py migrateto create the dgenome models.Insert the genomic data using the UCSC GTF file and the script: bin/load_genome_ucsc.py
Package: d-geolib
GEOLib is a Python package to generate, execute and parse several D-Serie and D-GEO Suite numerical models.InstallationInstall GEOLib with:$pipinstalld-geolibConfigure your environment using the instructions on ourSetuppage.
You may get the console executables from the Deltares download portal, or in the case of the D-GEO Suite, you may copy the contents of the installation ‘bin’ directory to your console folder.Running the source codeIf you want to make changes to GEOLib you can run the source code from GitHub directly on your local machine,
please follow the instructions below on how to set up your development environment using pip or poetry.You do not need to follow these instructions if you want to use the GEOLib package in your project.RequirementsTo install the required dependencies to run GEOLib code, run:$pipinstall-rrequirements.txtOr, when having poetry installed (you should):$poetryinstallTesting & DevelopmentMake sure to have the server dependencies installed:$poetryinstall-EserverIn order to run the testcode, from the root of the repository, run:$pytestor, in case of using Poetry$poetryrunpytestRunning flake8, mypy is also recommended. For mypy use:$mypy--config-filepyproject.tomlgeolibRunning standard linters is advised:$poetryrunisort.$poetryrunblack.DocumentationIn order to run the documentation, from the root of the repository, run:$cddocs$sphinx-build.build-bhtml-c.The documentation is now in thebuildsubfolder, where you can open
theindex.htmlin your browser.Build wheelTo build a distributable wheel package, run:$poetrybuildThe distributable packages are now built in thedistsubfolder.Update requirements.txtThe requirements.txt file is generated by poetry based on the pyproject.toml and poetry.lock files. In order to update/regenerate this file, run:$poetryinstall$poetryexport-Eserver-frequirements.txt--outputrequirements.txt--without-hashes$poetryexport-Eserver-frequirements.txt--outputrequirements-dev.txt--withdev--without-hashesCode linterIn order to run code cleanup/linter use the following commands:$poetryrunisort.$poetryrunblack.
Package: d-geolib-plus
GEOLib+ is a Python package to read, interprent and plot cpt files.
The package can also be used to get soil parameters for constitutive models.InstallationInstall GEOLib+ with:$pipinstalld-geolib-plusRequirementsTo install the required dependencies to run GEOLib+ code, run:$pipinstall-rrequirementsOr, when having poetry installed (you should):$poetryinstallTesting & DevelopmentMake sure to have the server dependencies installed:$poetryinstall-EserverIn order to run the testcode, from the root of the repository, run:$pytestor, in case of using Poetry$poetryrunpytestRunning flake8, mypy is also recommended. For mypy use:$mypy--config-filepyproject.tomlgeolibDocumentationIn order to run the documentation, from the root of the repository, run:$cddocs$sphinx-build.build-bhtml-c.The documentation is now in thebuildsubfolder, where you can open
theindex.htmlin your browser.Build wheelTo build a distributable wheel package, run:$poetrybuildThe distributable packages are now built in thedistsubfolder.
Package: dg-face-parsing
Face ParsingGenreate face parsing with BiSeNet。Installpipinstalldg_face_parsingUninstallpipuninstalldg_face_parsingparsing_faceparsing_facesUsageface_parsing.parsing_face(input_path, output_path)
*input_img- Image data()PIL.image.
*output_path- File path of output image(string, Blank as default).Opional, if output_path is blank, result image returned as cv2.image. Else, result image saved in output path and True returned.
*Return- Image data || Nonesface_parsing.parsing_faces(input_folder, output_folder)
*Return- None
*input_folder- File path of input folder(string).
*output_folder- File path of output folder(string).
Package: dgf-translate
dgf_translateThis Python Library deals with reading DGF Files and converting them into a usable GIS File format that can be imported into GDAL, Fiona and other Geospatial libraries.InstallationUse the package managerpipto install.pipinstalldgf_translateUsagefromdgf_translateimportdgf_to_geojson# returns geojson file pathdgf_to_geojson('/home/usr/file.geojson','/home/usr/file.geojson',in_crs=25833)## ContributingPullrequestsarewelcome.Formajorchanges,pleaseopenanissuefirsttodiscusswhatyouwouldliketochange.Pleasemakesuretoupdatetestsasappropriate.
Package: dgg-bot
A library for connecting to and making a bot in Destiny.gg chat.InstallingPython 3.9 or higher is required(version 0.5.0 and above, Python 3.8+ for versions below)# Linux/macOSpython3-mpipinstall-Udgg-bot# Windowspy-3-mpipinstall-Udgg-botUsageNot sure what to put here at this point in time. Unauthorized chat bots are subject to beingbanned, ask Cake in DGG for permission and guidelines for chat bots before running one.ExamplesA simple bot with three commands and will yump back at chatters.fromdggbotimportDGGBotimporttimebot=DGGBot("AUTH_TOKEN",owner="Owner",prefix="$",)# default command prefix is "!"@bot.command()@bot.is_owner()# only the owner named in DGGBot can use this command.deftest(msg):# $testmsg.reply("Test 123")@bot.command(aliases=["banmeplease"])# aliases for this commanddefbanme(msg):# $banme / $banmepleasebot.send("RightToBearArmsLOL BINGQILIN nathanTiny2")defis_cake(msg):# a check where only the user Cake can use commands with this checkreturnmsg.nick=="Cake"@bot.command(aliases=["oooo"])@bot.check(is_cake)defpog(msg):msg.reply("Cake OOOO")"""
Events
You can either name the function after the event, or include the event name in the decorator.
mention() is also included as a shortcut for event("on_mention").

Event names: on_ban, on_broadcast, on_join, on_mention, on_msg, on_mute, on_privmsg, on_quit,
             on_refresh, on_unban
"""@bot.event()defon_msg(msg):print(msg)# @bot.event("on_mention")@bot.mention()defyump(msg):if"MiyanoHype"inmsg.data:time.sleep(0.5)msg.reply(f"{msg.nick}MiyanoHype")if__name__=="__main__":bot.run_forever()Connecting to alternative DGG environments.fromdggbotimportDGGBotbot=DGGBot(owner="Owner",prefix="$",sid="SID",rememberme="REMEMBERME",config={{"wss":"wss://chat.omniliberal.dev/ws","wss-origin":"https://www.omniliberal.dev","baseurl":"https://www.omniliberal.dev","endpoints":{"user":"/api/chat/me","userinfo":"/api/userinfo"},"flairs":"https://cdn.omniliberal.dev/flairs/flairs.json",}},)@bot.event()defon_msg(msg):print(msg)if__name__=="__main__":bot.run_forever()
Package: dgi
dgipyFerramenta para facilitar o acesso aos dados disponibilizados pelo DGI/INPE.InstalaçãoO dgipy pode ser instalado em sistemas operacionais Linux, Windows e MacOS com versões do Python 3.7 ou superior. Para tal, o seguinte comando pode ser utilizadopipinstalldgiCaso queira é possível também baixar o projeto e fazer a instalação a partir do código fonte, para isto utilize os seguintes comandosgitclonehttps://gitlab.com/labisa/catalogo-de-imagens/dgipycddgipy
pipinstall-rrequirements.txt
pythonsetup.pyinstallExemplo de utilizaçãoA utilização do dgipy é dividida em três partes, estas apresentadas abaixo:(i) População do banco de dadosA primeira etapa necessária para a utilização do dgipy é a população do banco de dados em que o dgipy vai consumir, com todas as imagens disponíveis no DGI/INPE.Para esta etapa é possível definir diferentes regiões e datas de busca. O exemplo de código abaixo recupera imagens geradas pelo CBERS-4, de todo o território brasileiro para o intervalo de datas de 01/01/2019 até 25/01/2019fromdgi.catalogoimportCatalogoDGIdata_de_busca={"inicial":"01/01/2019","final":"25/01/2019"}localizacoes={"norte":"11.04024846","sul":"-36.49917303","leste":"-30.77297895","oeste":"-74.80618208"}catalogo=CatalogoDGI()catalogo.lista_imagens_dgi_regiao("CB4","",data_de_busca,localizacoes)(ii) Consumo dos serviços do DGI/INPECom o banco de dados populado, o dgipy consegue realizar buscas das imagens que foram registradas no banco e também fazer pedidos no catálogo.O código abaixo busca todas as imagens que possuem data entre os dias 01/01/2019 e 01/02/2019 e realiza o pedido dessas no catálogofromdgi.acessoimportBuscaDeImagemNoCatalogo,FacilitaDGIfacilita_dgi=FacilitaDGI("SEU_USUARIO_DO_CATALOGO","SUA_SENHA_DO_CATALOGO")catalogo_de_imagens_local=BuscaDeImagemNoCatalogo()# Fazendo a buscaimagens=catalogo_de_imagens_local.busca_imagens_por_data("01/01/2019","01/02/2019")# Realizando o pedido com as imagens encontradasinformacoes_do_pedido=facilita_dgi.realiza_pedido(imagens)(iii) Download das imagensCom o pedido realizado, você receberá um link, onde estarão todas as imagens disponíveis para odownload, para esta etapa o dgipy pode te ajudar com odownload.Para tal, basta pegar o link que o DGI/INPE te enviou e inserir no código abaixo.fromdgi.acessoimportBuscaDeImagemNoCatalogo,FacilitaDGIfacilita_dgi=FacilitaDGI("SEU_USUARIO_DO_CATALOGO","SUA_SENHA_DO_CATALOGO")imagens_ja_baixadas=facilita_dgi.baixa_imagens_de_link_do_pedido("LINK_DO_PEDIDO","LOCAL_ONDE_SERA_SALVO")Note que para a execução dos exemplos apresentados acima, é assumido que existe uma instância do MongoDB rodando em sua máquina. Para detalhes mais completos sobre toda a configuração do ambiente necessário para utilizar o dgipy, consulte adocumentação do projetoDocumentaçãoPara aprender mais sobre o dgipy e suas possibilidades de utilização, consulte adocumentação do projeto.
Package: dgii-rnc
Dataset de los RNCs de la DGII de República DominicanaHerramienta sencilla para carga el dataset de los RNCs de la DGII.Puede ser útil para actualizar rápidamente la información en los flujos de trabajo.Fuente del dataset:https://www.dgii.gov.do/app/WebApps/Consultas/RNC/DGII_RNC.zipDependencias- PolarsModo de UsoCargar dataset de RNCs>>>fromdgii_rnc.dgii_rncimportdgii_handler>>>importpolarsaspl>>>df=dgii_handler.rnc_df()>>>df.shape(728522,7)>>>df.filter(pl.col("NOMBRE").str.contains("BANCO CENTRAL"))shape:(4,7)+-----------+-------------------------------------------------------------------+----------------------------------+--------------------------------+------------+--------------+--------+|ID|NOMBRE|NOMBRE_COMERCIAL|CATEGORIA|FECHA|REGIMEN_PAGO|ESTADO||---|---|---|---|---|---|---||str|str|str|str|str|str|str|+========================================================================================================================================================================================+|401007551|BANCOCENTRALDELAREPUBLICADOMINICANA|null|SERVGRALESDELAADMPÚBLICA|23/10/1947|ACTIVO|NORMAL||430027715|ARSPLANSALUDBANCOCENTRAL|ARSBANCOCENTRAL|ADMINISTRACIONDERIESGOSDES|23/06/2003|ACTIVO|NORMAL||401508583|FONDODEJUBILACIONESYPENSIONESDELPERSONALDELBANCOCENTRAL|null|ADMINISTRACIÓNDEFONDOSDEPE|24/02/1999|ACTIVO|NORMAL||430118591|CLUBEMPLEADOSDELBANCOCENTRAL|CLUBEMPLEADOSDELBANCOCENTRAL|SERV.DEORGANIZACIÓN,DIRECCI|08/09/2011|ACTIVO|NORMAL|+-----------+-------------------------------------------------------------------+----------------------------------+--------------------------------+------------+--------------+--------+Convertir en dataframe de pandas>>>df=df.to_pandas()
Package: dgim
Python implementation of the DGIM algorithm:
a compact datastructure to estimate the number ofTruestatements in the last N elements of a boolean stream.FeaturesEstimation of the number of “True” statements in the last N element of a boolean streamLow memory footprint.Tunable error rate (the lower the error rate, the higher the memory footprint)ApplicationsWhen processing large streams of data such as clicks streams, server logs, financial streams.
It is often necessary to maintain statistics about the N latest elements.
If N is big or if you have many streams to process, it is not possible to store
the N latest elements.In such situations, if the processed stream is made of boolean,
the DGIM algorithm can help you estimate the number ofTruestatements
in the last elements.For instance, if the stream is made of server logs,
DGIM algorithm can estimate the proportion of visits that come from search engines.
(as opposed to direct access, or access through paid search)InstallationAt the command line:$ pip install dgimUsageSample code:from dgim import Dgim
dgim = Dgim(N=32, error_rate=0.1)
for i in range(100):
    dgim.update(True)
dgim_result = dgim.get_count() # 30 (exact result is 32)Documentationhttps://dgim.readthedocs.org.LicenseThe project is licensed under the BSD license.AuthorsSimon Dollé <simon.dolle@gmail.com>No other contributor yet. Why not joining?How to contributeCheck for open issues or open a fresh issue to start a discussion around a feature idea or a bug.Forkthe repositoryon GitHub to start making your changes to themasterbranch (or branch off of it).Write a test which shows that the bug was fixed or that the feature works as expected.Send a pull request and bug the maintainer until it gets merged and published. :) Make sure to add yourself toAUTHORS.ReferencesDatar, Mayur, et al. “Maintaining stream statistics over sliding windows.”
SIAM Journal on Computing 31.6 (2002): 1794-1813.Rajaraman, Anand, and Jeffrey David Ullman. Mining of massive datasets. Cambridge University Press, 2011. Chapter 4.http://infolab.stanford.edu/~ullman/mmds/ch4.pdfMining of Massive Datasets Coursera MOOC:http://infolab.stanford.edu/~ullman/mmds/ch4.pdfHistory0.2.0 (2015-01-05)Improved documentationMake most methods and attribute private.0.1.0 (2015-01-04)First release on PyPI.