Saravanan666/pypi-org huggingface dataset:
Package: dash-datatables
dash-datatablesMinimalDashwrapper for the wonderfulDataTables.netCode Snippetfrom dash import html
import dash_datatables as ddt

import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/solar.csv')

column_defs = [{"title": i, "data": i} for i in df.columns]

layout = html.Div([
    html.H2('US Solar Capacity'),
    html.Br(),
    ddt.DashDatatables(
        columns=column_defs,
        data=df.to_dict('records'),
        width="100%",
        order=[2, 'asc'],
    )
])InstallationYou can installdash-datatableswithpip:pip install dash-datatablesDocumentationHead over to theREADMEfor more details.ContributingThe source code fordash-datatablesis availableon GitHub. If you find a bug or something is unclear, we encourage
you to raise an issue. We also welcome contributions, to contribute, fork the
repository and open apull request.
Package: dash-datetimepicker
dash-datepickerThis is a dash datetime range picker based onreact-datetime.
In contrast to the dash datepicker from dash-core-components, it is also possible to specify hours and minutes on a certain day.Development# Create and exec into docker imagedocker-composeup
dockerexec-itdash-datepickerbash# From within the docker image run (only once when starting the project)cookiecutterhttps://github.com/plotly/dash-component-boilerplate.git# From within dockercddash_datetimepicker# Start python example app.venv/bin/activate
pipinstall-rrequirements.txt
pythonusage.py# Start react appnpmstart# Build datepickernpmrunbuildTest/publish created package# Test fresh builddeactivate
pythonsetup.pysdist
pipinstalldist/dash_datetimepicker-0.0.6.tar.gz
pythonusage.py# eventually create pypi accounttwinecheckdist/*
twineuploaddist/*

rm-rfdist# eventually authenticate at npmnpmlogin
npmpublish# check published modulepipinstalldash_datetimepicker
pythondash_datetimepicker/usage.py
Package: dash-deckgl
Package: dashDeckgl
Title: Deck.gl component for Dash
Version: 0.5.2
Description: Deck.gl component for Dash
Depends: R (>= 3.0.2)
Imports:
Suggests:
License: MIT + file LICENSE
URL:https://github.com/oceanum-io/dash-deckglBugReports:https://github.com/oceanum-io/dash-deckgl/issuesEncoding: UTF-8
LazyData: true
KeepSource: true
Package: dash-defer-js-import
Dash Defer JS ImportDash Defer JS Import is a Dash component library.Its purpose is to defer loading JS scripts until after the Dash React elements have loaded.InstallationThe package has been released to pypi and npm.
Easy thing is to install using pip:pip install dash_defer_js_importTo install from source, clone the repo andnpm install
npm run build:all
python setup.py sdist
pip install dist/dash_defer_js_import-0.0.1.tar.gzNote: his assumes you have the requirements already installed, if not, see requirements.txt
Might want to use venv to not mess up your own repo ...)Usage:import dash_defer_js_import as dji

# Dash server initi and layout setup
# ...

app.layout = html.Article(dji.Import(src="./path/to/script.js"))CreditsThis component is based on thegrasia-dash-componentIt was initialized using cookiecutter thanks to thehttps://github.com/plotly/dash-component-boilerplate.gitcookiecutter .
Package: dash-design-kit
dash-design-kitdash-design-kit is a part ofDash Enterprise,
Plotly's end-to-end Dash application platform. This public copy of the package
is a stub published on pypi.org designed to shield Dash Enterprise users fromdependency confusion,
a remote code execution attack publicly disclosed in 2021 by Alex Birsan.If you are a Dash Enterprise user, you will only see this public package if your
app is not properly configured to find the local package repository built into
Dash Enterprise. Visit the docs for your Dash Enterprise installation for details.If you never see this public package, it's still doing its job of protecting you
from the dependency confusion attack!
Package: dash-devextreme
dash-devextremeWrapper ofDevExtremecomponents for PlotlyDashInstallation$pipinstalldash_devextremeUsageimportdashimportdash_devextremeasddximportdash_html_componentsashtmldata=[{'id':1,'name':'Sales','text':'abc'},{'id':2,'parent_id':1,'name':'Sales 1','text':'abc'},{'id':3,'parent_id':1,'name':'Sales 2','text':'abc'},{'id':4,'name':'Developers','text':'abc'},{'id':5,'parent_id':4,'name':'Dev 1','text':'abc'},]app=dash.Dash('')app.scripts.config.serve_locally=Trueapp.layout=html.Div(children=[ddx.TreeList(dataSource=data,columns=[{'dataField':'name','width':290},{'dataField':'text'},],keyExpr='id',parentIdExpr='parent_id',width='100%')])if__name__=='__main__':app.run_server(debug=True)DashGo to this link to learn aboutDash.See thedash-component-boilerplaterepo for more information.LicenseMIT
Package: dash-dvx
dash-dvx - Dash Wrapper for DevExtreme componentsInstallationpip install dash-dvxPresentationThis package provides a ready-to-use component wich allows developers to use and interact with DevExtreme DataGrid and TreeList in a Dash app.Source code and documentationhttps://github.com/azur-scd/dash_dvxLicenceThis code is under MIT License.
Package: dash-embedded
dash-embeddeddash-embedded is a part ofDash Enterprise,
Plotly's end-to-end Dash application platform. This public copy of the package
is a stub published on pypi.org designed to shield Dash Enterprise users fromdependency confusion,
a remote code execution attack publicly disclosed in 2021 by Alex Birsan.If you are a Dash Enterprise user, you will only see this public package if your
app is not properly configured to find the local package repository built into
Dash Enterprise. Visit the docs for your Dash Enterprise installation for details.If you never see this public package, it's still doing its job of protecting you
from the dependency confusion attack!
Package: dash-enterprise
dash-enterprisedash-enterprise is a part ofDash Enterprise,
Plotly's end-to-end Dash application platform. This public copy of the package
is a stub published on pypi.org designed to shield Dash Enterprise users fromdependency confusion,
a remote code execution attack publicly disclosed in 2021 by Alex Birsan.If you are a Dash Enterprise user, you will only see this public package if your
app is not properly configured to find the local package repository built into
Dash Enterprise. Visit the docs for your Dash Enterprise installation for details.If you never see this public package, it's still doing its job of protecting you
from the dependency confusion attack!
Package: dash-enterprise-libraries
dash-enterprise-librariesdash-enterprise-libraries is a part ofDash Enterprise,
Plotly's end-to-end Dash application platform. This public copy of the package
is a stub published on pypi.org designed to shield Dash Enterprise users fromdependency confusion,
a remote code execution attack publicly disclosed in 2021 by Alex Birsan.If you are a Dash Enterprise user, you will only see this public package if your
app is not properly configured to find the local package repository built into
Dash Enterprise. Visit the docs for your Dash Enterprise installation for details.If you never see this public package, it's still doing its job of protecting you
from the dependency confusion attack!
Package: dasher
dasher: Generate interactive plotly dash dashboards in an instantInstallationpip install dasherYou can also install the in-development version with:pip install https://github.com/mfaafm/dasher/archive/master.zipFirst stepsCreating a simple, interactive dashboard with a nice layout is as easy as this:fromdasherimportDasherimportdash_html_componentsashtmlapp=Dasher(__name__,title="My first dashboard")@app.callback(_name="My first callback",_desc="Try out the widgets!",_labels=["Greeting","Place"],text="Hello",place=["World","Universe"],)defmy_callback(text,place):msg="{}{}!".format(text,place)return[html.H1(msg)]if__name__=="__main__":app.run_server(debug=True)The resulting dashboard looks like this:The code for this dashboard can be found inexamples/readme_example.py.DocumentationTo view the full project documentation, visithttps://dasher.readthedocs.io/.LicenseFree software,MIT LicenseDevelopmentTo run the all tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.3.1 (2019-12-17)Update and fix documentation.Fix documentation build using.readthedocs.yml.0.3.0 (2019-12-15)Generateidproperty fromnamefor every callback. Theidis now used to
identify the callback, whilenameis used in the layout for displaying.0.2.0 (2019-11-03)Use cookiecutter to create a proper project structure.Refactor core functionality intodasher.Api.Combine widget factory and template logic into unified layout implementation.Fix resizing bug when switching tabs by using callback-based tab switching.Add support of fully custom widgets.Add documentation.Add more examples.0.1.2 (2019-07-16)Add_labelsargument to thecallbackdecorator to enable customization of
widget labels.0.1.1 (2019-06-10)Addcreditsargument to DasherStandardTemplate to toggle whether to show credits
in the navbar.Update docstrings and documentation.Add margin to navbar.
Package: dash-escher
dash-escherdash-escher is a Dash component library. It rendersEschermetabolic networks utilizing theEscher Javascript API.Get started with:Install Dash and its dependencies:https://dash.plotly.com/installationRunpython usage.pyVisithttp://localhost:8050in your web browserContributingSeeCONTRIBUTING.mdInstall dependenciesIf you have selected install_dependencies during the prompt, you can skip this part.Install npm packages$ npm installCreate a virtual env and activate.$ virtualenv venv
$ . venv/bin/activateNote: venv\Scripts\activate for windowsInstall python packages required to build components.$ pip install -r requirements.txtInstall the python packages for testing (optional)$ pip install -r tests/requirements.txtWrite your component code insrc/lib/components/DashEscher.react.js.The demo app is insrc/demoand you will import your example component code into your demo app.Test your code in a Python environment:Build your code$ npm run buildRun and modify theusage.pysample dash app:$ python usage.pyWrite tests for your component.A sample test is available intests/test_usage.py, it will loadusage.pyand you can then automate interactions with selenium.Run the tests with$ pytest tests.The Dash team uses these types of integration tests extensively. Browse the Dash component code on GitHub for more examples of testing (e.g.https://github.com/plotly/dash-core-components)Add custom styles to your component by putting your custom CSS files into your distribution folder (dash_escher).Make sure that they are referenced inMANIFEST.inso that they get properly included when you're ready to publish your component.Make sure the stylesheets are added to the_css_distdict indash_escher/__init__.pyso dash will serve them automatically when the component suite is requested.Review your codeCreate a production build and publish:Build your code:$ npm run buildCreate a Python distribution$ python setup.py sdist bdist_wheelThis will create source and wheel distribution in the generated thedist/folder.
SeePyPAfor more information.Test your tarball by copying it into a new environment and installing it locally:$ pip install dash_escher-0.0.1.tar.gzIf it works, then you can publish the component to NPM and PyPI:Publish on PyPI$ twine upload dist/*Cleanup the dist folder (optional)$ rm -rf distPublish on NPM (Optional if chosen False inpublish_on_npm)$ npm publishPublishing your component to NPM will make the JavaScript bundles available on the unpkg CDN. By default, Dash serves the component library's CSS and JS locally, but if you choose to publish the package to NPM you can setserve_locallytoFalseand you may see faster load times.Share your component with the community!https://community.plotly.com/c/dashPublish this repository to GitHubTag your GitHub repository with the plotly-dash tag so that it appears here:https://github.com/topics/plotly-dashCreate a post in the Dash community forum:https://community.plotly.com/c/dash
Package: dashex
DescriptionYou have a staging infrastructure and a production infrastructure?  Wouldn’t
you like to store your monitoring configurations in a single place?  Wouldn’t
you like to use “infra as code” to get versionning, leave an audit trail,
back-up your configurations, etc.?If you answered yes to any of those questions, this tool is for you!In short,dashexis a command-line tool that allows you to:save/export your monitoring configuration to disk in a normalized format;load/import your monitoring configuration from disk to your monitoring
infrastructure.The storage on disk is a text-based format designed to be “easily” diffeable,
allowing you to review/audit changes.  You can store this in Git to submit
monitoring configuration changes as pull requests, have your peers review your
changes, deploy them as post-commit hooks, etc.ContributingThis project uses permissive licensing.  Nobody is paid to support/maintain
this software.  You may request support in the issue tracker, but be prepared
to submit a pull request to fix bugs and/or submit improvements.
Package: dash-extensions
Thedash-extensionspackage is a collection of utility functions, syntax extensions, and Dash components that aim to improve the Dash development experience. It can be divided in four main pillars,Theenrichmodule, which contains various enriched versions of Dash componentsA number of custom components, e.g. theWebsocketcomponent, which enables real-time communication and push notificationsThejavascriptmodule, which contains functionality to ease the interplay between Dash and JavaScriptThesnippetsmodule, which contains a collection of utility functions (documentation limited to source code comments)Theenrichmodule enables a number oftransformsthat add functionality and/or syntactic sugar to Dash. Examples includeMaking it possible to avoid invoking a callbackif it is already runningvia theBlockingCallbackTransformEnabling logging from within Dash callbacks via theLogTransformImproving app performance via theServersideOutputTransformto name a few. To enable interactivity, the documentation has been moved to aseparate page.NB: The 1.0.0 version introduces a number of breaking changes, see documentation for details.Donation
Package: dash-fcast
Dash-Forecast allows you to easily build forecasting dashboards.Why Dash-ForecastJudgment and decision making research shows thatvisual tools are an easy and effective way to boost forecasting accuracy. Dash-Forecast is a high-level API for creating beautiful forecasting visualizations and statistical summaries.Installation$ pip install dash-fcastQuickstartIn just a few lines of code, we'll create an app that gives you:An intuitive 'bounds and moments' forecast elicitationAn editable data table representation of the forecastProbability density function and cumulative distribution function line plots of the forecastA bar plot of the data tableCreate a fileapp.py:importdash_fcastasfcastimportdash_fcast.distributionsasdistimportdashimportdash_bootstrap_componentsasdbcimportdash_core_componentsasdccimportdash_html_componentsashtmlimportplotly.graph_objectsasgofromdash.dependenciesimportInput,Outputapp=dash.Dash(__name__,external_stylesheets=[dbc.themes.BOOTSTRAP])app.layout=html.Div([html.Br(),dist.Moments(id='Forecast'),html.Br(),fcast.Table(id='Table',datatable={'editable':True,'row_deletable':True},row_addable=True),html.Div(id='graphs')],className='container')dist.Moments.register_callbacks(app)fcast.Table.register_callbacks(app)@app.callback(Output('graphs','children'),[Input(dist.Moments.get_id('Forecast'),'children'),Input(fcast.Table.get_id('Table'),'children')])defupdate_graphs(dist_state,table_state):distribution=dist.Moments.load(dist_state)table=fcast.Table.load(table_state)pdf=go.Figure([distribution.pdf_plot(),table.bar_plot('Forecast')])pdf.update_layout(transition_duration=500,title='PDF')cdf=go.Figure([distribution.cdf_plot()])cdf.update_layout(transition_duration=500,title='CDF')return[dcc.Graph(figure=pdf),dcc.Graph(figure=cdf)]if__name__=='__main__':app.run_server(debug=True)Run your application with:$pythonapp.pyOpen your browser and navigate tohttp://localhost:8050/.Citation@software{bowen2020dash-fcast,
  author = {Dillon Bowen},
  title = {Dash-Forecast},
  url = {https://dsbowen.github.io/dash-fcast/},
  date = {2020-09-11},
}LicenseUsers must cite this package in any publications which use it.It is licensed with the MITLicense.AcknowledgementsThe following collaborators deserve special acknowledgement:David Melgin, for the bounds and moments elicitationEzra Karger, whose non-parametric elicitation methods helped inspire my 'tabular elicitation'Sarah Reed, for feedback on the front-end designI would also like to thank the Tetlock Lab, whose weekly presentations inspired various aspects of this package, including Zachary Jacobs' and Ian Lustick's 'first approximation algorithm', Scott Page's multi-model thinking, and Annie Duke's presentation on intuitively eliciting predictions.
Package: dash-flask-keycloak
This purpose of this library is to provide seamless integration of Plotly Dash or Flask with keycloak via the python-keycloak package.PrerequisitesPrior to using this library, a Keycloak server must be setup. Please refer to the official documentation,https://www.keycloak.org/After setting up the server, create a realm and a client for the application.In the clients settings, write redirect URI as "your_app_url/keycloak/callback" .Installationpip install dash-flask-keycloakMotivationThe original project was abandoned and doesn't work with Keyloack higher than 17.0 version.Also were added:Session state controlAccess token validationSession lifetime (It's recommended to set "session_lifetime" in app less or equal to a minimal value among SSO Session Idle, SSO Session Max in Keyloak realm settings)You can find examples in dash-flask-keycloak/examples(Was developed and tested on Ubuntu 20.04, Python 3.8.10 and Keycloak 21.1.1)
Package: dash-flask-login
# Integration of Dash with Flask-Login### Intro
#### Dash-Flask-Login is a [Dash](https://github.com/plotly/dash) to allow integration with the popular [Flask-Login](https://github.com/maxcountryman/flask-login) for user session management.Dash-Admin is intended to be plug and play!  Providing user authentication should be as simple as:` auth = FlaskLoginAuth(dash_app) `### DocumentationFor now, please look at`usage_dash_flask_login.py`for an example of using login/logout forms created as separate Dash apps.  Look at`usage_dash_flask_login_with_default_views.py`to see an example with the default login/logout forms provided by the package.### InstallationDash-Flask-Login can be installed via`pip installdash-flask-login`.# WARNING:
## This package is provided without any warranty.  The responsibility of securely storing user data is the sole responsibility of the developer.  The creator of this package will not be held liable for any breach of security while using the package.
Package: dash-fluentui-components
dash-fluentui-componentsA component library for Plotly's Dash based on the fluentui react components.Installationpoetryadddash-fluentui-componentsor via pippipinstalldash-fluentui-componentsUsagefromdashimportDash,Input,Output,callback,callback_contextfromdash.exceptionsimportPreventUpdateimportdash_fluentui_componentsasdfcapp=Dash(__name__)open_dialog=dfc.Button("OpenDialog",id="open-id")close_dialog=dfc.Button("CloseDialog",id="close-id")dialog=dfc.Dialog("Content",title="A dialog component",trigger=open_dialog,trigger_action=close_dialog,)page1=dfc.Page(dfc.Button("Button 1"),page_key="page-1",controls=dialog)page2=dfc.Page(dfc.Button("Button 2"),page_key="page-2",controls=dfc.Button("Control 2"))app.layout=dfc.FluentProvider(theme="dark",children=dfc.PagesWithSidebar([page1,page2],selected_key="page-1"))if__name__=="__main__":app.run_server(debug=True)TODOsmooth sidebar transitions
Package: dash-geoman
dash-geomanWrapper ofleaflet-geomancomponents for PlotlyDashGetting startedThe easiest way to get started is to install the latest version of Dash and dash-geoman via pip.pipinstalldash==2.0.0
pipinstalldash-geomanOnce the installation is completed, paste the following lines of code into a .py file and run it.importdashimportdash_leafletasdlimportdash_geomanasdgmapp=dash.Dash()app.layout=dl.Map(childrenn=[dl.TileLayer(),dgm.Geoman(),],style={'width':'1000px','height':'500px'})if__name__=='__main__':app.run_server()If you visithttp://127.0.0.1:8050/in your browser, you should see aleafletmap anddash-geoman's edit controls.DocumentationBuild instructionsStart by cloning this repository,gitclonegit@github.com:pikhovkin/dash-geoman.gitcddash-geomanNext, create a virtual environment and install the python dependencies,python3-mvenvvenv&&.venv/bin/activate
pipinstall-rrequirements.txtFinally, install packages via npm (ignore errors) and run the build script,npmi--ignore-scripts
npmrunbuildLicenseMIT
Package: dash-gif-component
dash-gif-componentdash-gif-component provides a playable gif dash component that is built on top ofreact-gif-player.Plotly's dash documentation can be foundhere.BackgroundWhile there may be many possible use cases for using a gif in a dash app, the one reason that originally inspired me to add them was to provide a visual aid to show end-users how to use various interactive features.My original approach was to simply put a gif in an html<img>tag, but I quickly found out that this didn't work well in practice since the constant looping was a bit overwhelming, especially when there were multiple gifs on the same page.The second approach was a bit more refined, and I wrote a plotly community blog post about ithere. This used custom javascript that was dispatched when the window location's hash was changed and it worked just fine...that was until I learned that this caused very strange behavior in Internet Explorer when using dash > 0.39. There were also many other annoying issues when making this compatible with IE which made this a pain to maintain.That brings me to dash-gif-component -- the final chapter of this story. Finally, a simple to use python module that wont require you to write a single line of javascript or create any hacks. It works on the latest version of dash, and best of all, works in IE as is!Hope you enjoy using this plugin.InstallationYou can installdash-gif-componentwithpip:pip install dash-gif-componentDocumentationdash-gif-comonenthas only one component (GifPlayer) and it is really easy to setup. All you need to provide are two properties:gif: A string path to your gifstill: A string path to an image that is used for the "pause" mode*There is also an optional property calledautoplaywhich when set to true, will cause the gifs to automatically play. I find this to defeat the purpose of the component, but decided to include this for completeness when replicatingreact-gif-player.*Gifsicleis a useful cli-tool that can extract the first, or any, frame of a gif, among other gif related tasks.Note: if using local files for gif and still, then they must be loaded from the folder that is configured to serve static files. By default, this is set toassets.Usage example:importdash_gif_componentasgifimportdashimportdash_html_componentsashtmlapp=dash.Dash(__name__)app.layout=html.Div([gif.GifPlayer(gif='assets/my-gif.gif',still='assets/my-still.png',)])For a complete running example, please check outdemo/usage.pyCustom StylingWhile I do find the original style sheet to be excellent on its own, you may still want to make some tweaks here and there. There are only a handful of selectors and overwriting is straightforward. The stylesheet can be referenced insrc/lib/components/css/gifplayer.css.