Saravanan666/pypi-org huggingface dataset:
Package: diabric
# IntroductionDiabric is a python package containing utilities for use in Fabric fabfiles.These tasks, functions, and classes are useful for:- working with Amazon EC2- configuring machines- installing, configuring, and starting services, servers, and daemons, likenginx and supervisord.- deploying python wsgi web applications.- uploading and formatting files- creating python virtualenv virtual environments- installing packages into virtualenvs- configuring fabfilesThis package is still very much _alpha_ and exists to reduce code duplicationacross a number of fabfiles I have for different projects.# Requirements- Written with Python 2.7(http://python.org/download/releases/2.7.3/) in mind.- Packaged with distutils2 (http://packages.python.org/Distutils2/)- Fabric (http://docs.fabfile.org/) for creating fabfile tasks, working withremote hosts, ...- Boto (https://github.com/boto/boto) for interacting with EC2.# Installation## Install from pypi.python.orgDownload and install diabric.pip install diabric## Install from github.comInstall distutils2 if you do not yet have it installed.pip install distutils2Download and install diabric.cd ~git clone git@github.com:todddeluca/diabric.gitcd diabricpysetup install
Package: diadem
Diadem extends idem to streamline cloud resource management by organizing, parametrizing, and visualizing SLS data.AboutBuilt on POP, diadem integrates with Idem, providing a user-friendly cli interface for handling complex cloud infrastructures.
It extends idem with the subcommands “compile” and “view”, which grant idem the ability to do tasks based on complex
analysis of relationships between resources. Such as:Graphing the relationships between resourcesIdentifying orphansAbstracting the similarities between resources into paramsCreating arg-bind relationships between resourcesEach of these abilities enable you to easily get started with efficiently managing cloud resources with idem from ground zero.What is POP?This project is built withpop, a Python-based
implementation ofPlugin Oriented Programming (POP). POP seeks to bring
together concepts and wisdom from the history of computing in new ways to solve
modern computing problems.For more information:Intro to Plugin Oriented Programming (POP)pop-awesomepop-createGetting StartedPrerequisitesPython 3.10+git(if installing from source, or contributing to the project)InstallationNoteIf wanting to contribute to the project, and setup your local development
environment, see theCONTRIBUTING.rstdocument in the source repository
for this project.If wanting to usediadem, you can do so by either
installing from PyPI or from source.Install from PyPIInstall diadem with only the “compile” functionality:pipinstalldiademInstall diadem with matplotlib for data visualizations with matplotlib and networkx:pipinstalldiadem[visualize]Install from source# clone repogitclonegit@gitlab.com:Akm0d/diadem.gitcddiadem# Setup venvpython3-mvirtualenv.venvsource.venv/bin/activatepipinstall-e.UsageDiadem simplifies the management of cloud resources.
Use it to enumerate resources, organize SLS files, and visualize cloud infrastructure, ensuring efficient and secure cloud operations.ExamplesEnumerate cloud resources with idem describe:idemdescribe"aws.*">aws.slsidemdescribe"gitlab.*">gitlab.slsCreate argbind relationships between resources only:idemcompileaws.slsgitlab.sls--match-strategy=passParametrize sls files by creating arg-bind relationships and abstract similarities between resources into params:idemcompileaws.slsgitlab.slsParametrize sls files by creating arg-bind relationships, abstract similarities between resources into params, and organize the output into logical groups in a file tree:idemcompileaws.slsgitlab.sls--tree=/srv/idemVisualize relationships between resources using matplotlib:idemview/srv/idem/state--params/srv/idem/paramRun idem state on the newly crafted sls/param trees:idemstate/srv/idem/state--params/srv/idem/paramIdentify orphan resources and craft absent states for them:idemview/srv/idem/state--params/srv/idem/param>orphans.slsShow a graph of relationships between resources (requires having installed diadem with the “visualize” extras):idemview/srv/idem/state--params/srv/idem/param--showRoadmapReference theopen issuesfor a list of
proposed features (and known issues).AcknowledgementsImg Shieldsfor making repository badges easy.
Package: diadump
https://github.com/idlesign/diadumpОписаниеСкачиватель диафильмовПозволяет скачать оцифрованные изображения диафильмов с замечательного сайтаhttp://diafilmy.suдля последующего локального просмотра.;Скачиваемвседиафильмыраздела"Сказки"вподдиректориидиректорииdia$diadumpmanyhttp://diafilmy.su/diafilmy/skazki//home/idle/dia/;Скачиваемтолькоодиндиафильм-"Огниво"$diadumponehttp://diafilmy.su/5180-ognivo.html/home/idle/dia/УстановкаПриложение требует для работыPython 3.Чтобы установить приложение для Python 3 в операционной системе часто существует отдельная командаpip3,
ею и воспользуемся:$sudopip3installdiadumpПосле установки можно выполнять командуdiadump, как показано в описании выше.
Package: diagho-toolkit
Diagho ToolkitWhat is Diagho Toolkit ?Diagho Toolkit is a python library which will read and parse a genomic file and return a generator with a pydantic model.What database is used ?OMIMHGNCHPOgnomADgnomAD constraintVCFFastaGFF3What pydantic model is returned ?ClinvarFeatureGeneGnomADGnomAD_constraintPathologyRegionSequenceSymptomVariantInstallationInstall diagho-toolkit in pippip install diagho-toolkitIn your app if you want to parse a GFF3 file and get regions, you just need to call this methodfrom diagho_toolkit.gff3 import get_regions

get_regions(gff3_filename="gff3_file_path")And this method will return a Generator with a list of RegionModel that you can easily save into your database.Customize diagho-toolkitClone the project fromgithubBuild and run the docker imagemake dockerInto the container update and install requirementsmake requirements
make installCustomize the project in the repositorysrc/diagho_toolkitCreate and run testsmake testsUpdate documentation inREADME.mdandCHANGELOG.mdand version inpyproject.tomlLeave the container and commit your release into githubgit commit -am 'New commit'
git pushUpdate the version in test-PyPI ingithub-actionswith the workflowTest PyPi.When all is ready, run the workflowPypito update the official new version
Package: diagNNose
diagNNose ·Paper:https://arxiv.org/abs/2011.06819Demo:Documentation:https://diagnnose.readthedocs.ioThis library contains a set of modules that can be used to analyse the activations of neural networks, with a focus on NLP architectures such as LSTMs and Transformers.
In particular, it contains functionality for :Extractingactivations from different types of (language) models and providing quick access to these stored activations.Trainingdiagnostic classifiers(Hupkes et al., 2018) on extracted activations.Trainingcontrol tasks(Hewitt & Liang, 2019) parallel to these diagnostic classifiers.Performingmodel-agnosticfeature attributions(Murdoch et al., 2018) on a model.Running a broad linguistic suite oftargeted syntactic evaluationson a language model.:tada:diagNNosehas been presented atBlackboxNLP 2020! The paper can be foundhere.Documentationcan be found atdiagnnose.readthedocs.io.Our library is officially registered with pip and can be installed by runningpip install diagnnose.
The preferred version of Python is ≥3.6. The required packages are stated inrequirements.txt.Quick TourThe workflow ofdiagNNoseis divided into several building blocks, that can be combined for various experiments.We provide a few examples that demonstrate the library.
An interactive and more extensive interface for these scripts is also provided in the form of a notebook:Activation ExtractionThe activations of a model can be extracted using anExtractorthat takes care of batching and selecting activations of interest.Fine-grained activation selection is possible by defining aselection_func, that selects an activation based on the current sentence index and corpus item.fromtorchtext.dataimportExamplefromdiagnnose.configimportcreate_config_dictfromdiagnnose.corpusimportCorpusfromdiagnnose.extractimportExtractorfromdiagnnose.modelsimportLanguageModel,import_modelfromdiagnnose.tokenizer.createimportcreate_tokenizerif__name__=="__main__":config_dict=create_config_dict()tokenizer=create_tokenizer(**config_dict["tokenizer"])corpus:Corpus=Corpus.create(tokenizer=tokenizer,**config_dict["corpus"])model:LanguageModel=import_model(**config_dict["model"])defselection_func(w_idx:int,item:Example)->bool:returnw_idx==item.extraction_idxextractor=Extractor(model,corpus,selection_func=selection_func,**config_dict["extract"])activation_reader=extractor.extract()Research usingdiagNNoseJumelet, Zuidema & Hupkes (2019):Analysing Neural Language Models: Contextual Decomposition Reveals Default Reasoning in Number and Gender AssignmentCitingIf you intend on usingdiagNNosefor your research, please cite us as follows. Feel free to reach out, we'd love to help!@inproceedings{jumelet-2020-diagnnose,
    title = "diag{NN}ose: A Library for Neural Activation Analysis",
    author = "Jumelet, Jaap",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.32",
    pages = "342--350",
}
Package: diagnostic
diagnosticdiagnosticmakes it easier to build command line tools with great error reporting.FeaturesUniquecodes on everyDiagnosticTooling to help ensure allcodes in the codebase are documentedFancy graphical output, built on top ofrich, with graceful fallbacks to ASCII and plain stringsPresent a causal chain if neededDocumentationSee thedocumentationfor more information.LicenseThis project is licensed under the terms of the MIT license.
Package: diagnosticism
Diagnosticism.PythonDiagnosticism library, for PythonTable of ContentsIntroductionInstallationComponentsExamplesProject InformationIntroductionT.B.C.Installation & usageInstall viapiporpip3, as in:pip3 install diagnosticismUse viaimport:importdiagnosticismComponentsDiagnosticism.Pythonprovides components in the following categories:Contingent ReportingDiagnostic LoggingTracingNOTE: for the moment, the Diagnostic Logging facilities emit to the standard error stream, via the Contingent Reporting API. In the near future this will be changed to work     with more sophisticated logging libraries, including the standard logging facilities and the (as yet to be release)Pantheios.Python.ExamplesExamples are provided in theexamplesdirectory, along with a markdown description for each. A detailed list TOC of them is provided inEXAMPLES.md.Project InformationWhere to get helpGitHub PageContribution guidelinesDefect reports, feature requests, and pull requests are welcome onhttps://github.com/synesissoftware/Diagnosticism.Python.DependenciesRelated projectsDiagnosticism.GoDiagnosticism.NETLicenseDiagnosticism.Pythonis released under the 3-clause BSD license. SeeLICENSEfor details.
Package: diagnostics-endpoint
Diagnostics EndpointLibrary to generate diagnostics endpoint for flask based python web applications.InstallationInstallation from PyPithis package can be installed from PyPi using pip :pipinstall-Udiagnostics-endpointInstallation from GithubInstall this package directly from github:pipinstall-Ugit+ssh://git@github.com/shardulsrivastava/python-diagnostics.git@masterUsageAdd the dependency in requirements.txt.diagnostics-endpoint==0.0.7Add the below line to your application entry point:fromdiagnostics_endpointimportDiagnosticsapplication_endpoints=[{"name":"API - Test Service","endpoint":"http://my-api"},{"name":"Upstream - UpStream API","endpoint":"https://my-upstream-api"},{"name":"Downstream - DownStream API","endpoint":"https://my-downstream-api"},{"name":"Database - MyDatabase","endpoint":"tcp://my-database-host:my-database-port"}]app=Flask(__name__)Diagnostics.render(app,application_endpoints)This will generate two endpoints :/diagnosticsas/heartbeatwith response asOkConfigurationdiagnostics-endpointssupports configuration viaDiagnostics.render()method.Set Application EndpointsDiagnostics.render()method takes a list of dictionaries of application endpoints. Format of the dictionary is :application_endpoints=[{"name":"API Name","endpoint":"API URL"}]here endpoint can be ahttp,httpsortcpendpoint. You can also give absolute URLs as well by giving the url directly as/user.
Example:application_endpoints=[{"name":"My API Endpoint","endpoint":"/hello"},{"name":"My HTTP Endpoint","endpoint":"http://example.com/healthcheck"},{"name":"My HTTPS Endpoint","endpoint":"https://example.com/healthcheck"},{"name":"My TCP Database Endpoint","endpoint":"tcp://my-database-host:3306"}]Set Application Rootyou can specify application root path as:Diagnostics.render(app,application_endpoints,"/myapplication")This will generate the two endpoints as/myapplication/diagnosticsand/myapplication/heartbeat.Python Diagnostics History
Package: diagonal
A small module for getting diagonals in a 2D Arrayimportdiagonalarr=[]#your arrayDefinitionget(arr, start = "0", direction = "up", type="all")start(single character)0 - start from the first rowm - start from m'th rowdirection(string)up - uses upward direction for traversaldown - downward direction for traversal**type **(string)all - default value which indicates that all diagonals are consideredmain - gives the main diagonal of square matrixanti - gives anti diagonal of square matrixzigzag - gives zigzag traversal of the matrixUsagesSquare matrixMain diagonaldiagonal.get(arr, type = "main")Anti diagonaldiagonal.get(arr, type = "anti")Also zigzags upwards and downwardsdiagonal.get(arr, type = "zigzag" ,direction ="up")Other matricesAll traversals except main and anti diagonalsEx:diagonal.get(arr, start = "0", direction = "up")gives all diagonals starting from the first elements traversed in upward direction.Return valuesAny errors return a stringMain and Anti diagonal traversals returns a listZigZag traversals return a listOthers return a list of list having individual diagonals
Package: diagralhomekit
DiagralHomekitAllow to control your Diagral alarm systems through Apple Homekit.First, you need to create a configuration file~/.diagralhomekit/config.iniwith connection details for all Diagral systems.[diagral:Home]name=[an explicit name for this system]login=[email address of the Diagral account]password=[password for the Diagral account]imap_login=[IMAP login for the email address receiving alarm alerts]imap_password=[IMAP password]imap_hostname=[IMAP server]imap_port=[IMAP port]imap_use_tls=[true/1/on if you use SSL for the IMAP connection]master_code=[a Diagral master code, able to arm or disarm the alarm]system_id=[system id — see below]transmitter_id=[transmitter id — see below]central_id=[central id — see below]system_id,transmitter_idandcentral_idcan be retrieved with the following command, that prepares a configuration file:python3-mdiagralhomekit--config-dir~/.diagralhomekit--create-config'diagral@account.com:password'Then you can run the script:python3-mdiagralhomekit--port6666--config-dir~/.diagralhomekit-v2On the first launch, a QR code is displayed and can be scanned in Homekit, like any Homekit-compatible device.You can send logs toLokiwith--loki-url=https://username:password@my.loki.server/loki/api/v1/push.
You can also send alerts toSentrywith--sentry-dsn=my_sentry_dsn.Everything can be configured by environment variables instead of arguments:DIAGRAL_PORT=6666DIAGRAL_CONFIG=/etc/diagralhomekitDIAGRAL_SENTRY_DSN=https://sentry_dsn@sentry.io/42DIAGRAL_LOKI_URL=https://username:password@my.loki.server/loki/api/v1/pushDIAGRAL_VERBOSITY=1As many sensitive data must be stored in this configuration file, so you should create a dedicated email address and Diagral account.Plex sensorA presence can be detected when a specified Plex player is playing something:[plex:appletv_web]server_token=[authentication token]server_url=[url of your Plex server]player_name=[Displayed name for the player]player_device=None,player_product=[Product name of the targeted player]player_title=[Title of the targeted player]player_address=[IP address of the targeted player]Only one of the last four properties is required to match with the targeted player.
To get actual property values, you can usecurl:curl-HAccept:application/json-HX-Plex-Token:[authenticationtoken][urlofyourPlexserver]/status/sessionsHTTP monitoringYou can monitor some websites, as air purifier sensors (no Homekit sensor is available for HTTP monitoring…):[internet:website]url=[url to check]name=[Displayed name]Weather monitoringYou can monitor weather, and emulate a presence when it will rain in the next 10 minutes:[meteofrance:paris]name=Parislatitude=48.866667longitude=2.333333country=FRregion=Île-de-FranceUPS monitoringUPS can also be monitoring, as soon as NUT is locally installed (standard UPS monitoring server on Linux.[ups:home]
name=eaton650
Package: diagram2vec
diagram2vecDiagram2vec provides the following finite-dimensional vector representations of the persistence diagrams:persistence statistics (statistics of quanitites such as persistence, midlife, mult.life)Euler characteristic curvek-th Betti number curvespersistence curves (curves of quanitites such as persistence, midlife, multitplicative life)entropy curves (curves of entropy of quanitites such as persistence, midlife, multitplicative life)Adcock-Carlsson polynomials (https://arxiv.org/abs/1304.0530)All representations have ε-robust versions, i.e. not taking into account intervals with persistence below a certain threshold function.InstallationDependenciespython (>= 3.6)numpyscipyThe latest version of the diagram2vec can be installed with pip:pip install diagram2vecExample of usageimportdiagram2vec# list of lists of ndarrays, representing a collection of persistence diagramsdiagrams=[[np.array([[0.0,np.inf],[0.0,0.4],[0.0,0.5]]),np.array([[0.1,0.6],[0.2,0.4]])],[np.array([[0.0,np.inf],[0.0,0.1]]),np.array([[0.4,0.7]])],]# compute a list of statistics of a distribution of the persistence p := (d - b) quantitystats=diagram2vec.statistics(diagrams,quantity="persistence",statistics=["min","mean","count"])# compute an Euler characteristic curve (a vector of dimension 10)euler_curve=diagram2vec.euler_curve(diagrams,m=10)# compute a Betti number curve, which is default quantity for the `persistence_curve`# (a vector of dimension 50, intervals with persistence below 0.05 are not used)betti_curve=diagram2dec.persistence_curve(diagrams,m=50,f="linear",b=0.05)# compute a curve of persistence quantity# available quanities: 'betti', 'persistence', 'midlife', 'multlife'persistence_curve=diagram2vec.persistence_curve(diagrams,quantity="persistence",m=50)# compute a curve of midlife entropy# available quanities: 'persistence', 'midlife', 'multlife'entropy_curve=diagram2vec.entropy_curve(diagrams,quantity="midlife",m=20)# compute Adcock-Carlsson polynomialspolynomials=diagram2vec.polynomials(diagrams,terms=[["birth","persistence"],["dmax","persistence"]],powers=[[1,1],[2,4]])
Package: diagram-autobuild
# diagram-autobuild![diagram-autobuild-demo](https://raw.githubusercontent.com/wiki/momijiame/diagram-autobuild/images/diagram-autobuild.gif)## What is this?diagram-autobuild improves the efficiency of the making diagrams.diagram-autobuild is supporting the following tools.- graphviz (http://www.graphviz.org/)- blockdiag (https://pypi.python.org/pypi/blockdiag)- nwdiag (https://pypi.python.org/pypi/nwdiag)- actdiag (https://pypi.python.org/pypi/actdiag)- seqdiag (https://pypi.python.org/pypi/seqdiag)- ERAlchemy (https://pypi.python.org/pypi/ERAlchemy)### BackgroundIf you make a diagram, you usually do the following cycle.1. Edit the source (e.g. \*.dot)2. Build the diagram (e.g. execute 'dot' command)3. Open the diagram4. Make sure whether the diagram is your intended (If it isn't so, retry from the first)This procedure is inefficient and bother you.### SolutionAutomation: the above procedure between 2 and 3diagram-autobuild observes the source file.If the source file is modified, rebuild the diagram, and the browser which indicates the diagram is reloaded.## How to installdiagram-autobuild is installed by using pip of Python's package manager.```$ pip install diagram-autobuild```or```$ pip install git+https://github.com/momijiame/diagram-autobuild.git```## How to useYou will be able to use 'diagram-autobuild' command.```$ diagram-autobuild --helpUsage: diagram-autobuild [OPTIONS] TOOL SOURCEOptions:--tool-opts TEXT                Options that are passed to the tool--open-browser / --no-open-browserOpen your default browser when the commandis executed--help                          Show this message and exit.```Execute 'diagram-autobuild' command with the tool name and the path of source file.(The tool must to be installed already)Example (graphviz):```$ lssample.dot$ diagram-autobuild graphviz sample.dot```Example (blockdiag):```$ lssample.diag$ diagram-autobuild blockdiag sample.diag```Example (ERAlchemy):```$ lssample.er$ diagram-autobuild eralchemy sample.er```If it succeed, open your default browser and you can see the diagram.
Package: diagrams
DiagramsDiagram as Code.Diagrams lets you draw the cloud system architecturein Python code. It was born forprototypinga new system architecture design without any design tools. You can also describe or visualize the existing system architecture as well. Diagrams currently supports main major providers including:AWS,Azure,GCP,Kubernetes,Alibaba Cloud,Oracle Cloudetc... It also supportsOn-Premisenodes,SaaSand majorProgrammingframeworks and languages.Diagram as Codealso allows you totrackthe architecture diagram changes in anyversion controlsystem.NOTE: It does not control any actual cloud resources nor does it generate cloud formation or terraform code. It is just for drawing the cloud system architecture diagrams.ProvidersGetting StartedIt requiresPython 3.7or higher, check your Python version first.It usesGraphvizto render the diagram, so you need toinstall Graphvizto usediagrams. After installing graphviz (or already have it), install thediagrams.macOS users can download the Graphviz viabrew install graphvizif you're usingHomebrew.# using pip (pip3)$pipinstalldiagrams# using pipenv$pipenvinstalldiagrams# using poetry$poetryadddiagramsYou can start withquick start. Check outguidesfor more details, and you can find all available nodes list inhere.ExamplesEvent ProcessingStateful ArchitectureAdvanced Web ServiceYou can find all the examples on theexamplespage.ContributingTo contribute to diagram, check outcontribution guidelines.Let me know if you are using diagrams! I'll add you in showcase page. (I'm working on it!) :)Who uses it?GitPitchis the perfect slide deck solution for Tech Conferences, Training, Developer Advocates, and Educators. Diagrams is now available as a dedicatedCloud Diagram Markdown Widgetso you can use Diagrams directly on any slide for conferences, meetups, and training.Cloudiscoveryhelps you to analyze resources in your cloud (AWS/GCP/Azure/Alibaba/IBM) account. It allows you to create a diagram of analyzed cloud resource map based on this Diagrams library, so you can draw your existing cloud infrastructure with Cloudiscovery.Airflow Diagramsis an Airflow plugin that aims to easily visualise your Airflow DAGs on service level from providers like AWS, GCP, Azure, etc. via diagrams.Other languagesIf you are familiar with Go, you can usego-diagramsas well.LicenseMIT