Saravanan666/pypi-org huggingface dataset:
Package: dash-interactive-graphviz
Dash Interactive GraphvizDash Interactive Graphviz renders thegraphvizor dot language in a dash component.Graphviz is open source graph visualization software. Graph visualization is a way of representing structural information as diagrams of abstract graphs and networks. It has important applications in networking, bioinformatics, software engineering, database and web design, machine learning, and in visual interfaces for other technical domains.It supports:Displaying graphviz sources as a graphRendering is fully client sideZooming and Panning around the graph (With a reset)Selecting nodesAnimation between different graphsUsageYou can install with pip (or anything else)pipinstalldash_interactive_graphvizUsage is simple, below shows how to instantiate the component on its own.importdash_interactive_graphvizdot_source="""digraph  {node[style="filled"]a ->b->da->c->d}"""dash_interactive_graphviz.DashInteractiveGraphviz(id="graph",dot_source=dot_source)When a node or edge is clicked theselected_nodeorselected_edgeproperty will change.NOTE[Pending deprecation]selectedis replaced byselected_node.Hooks on selected graph elements allow you to change elements of your Dash app when a graph element is selected. For example:@app.callback(Output(...,...),[Input('graph','selected_node')])defchange_my_view(selected):# Do something with selectedYou can see a basic example in usage.py.Often you may want to update the dot_source based on the selected graph element. This is supported, and you can see an example in usage_highlighting.py.You can change the layout engine through theengineprop. Seehttps://github.com/magjac/d3-graphviz#graphviz_enginefor more information.BehaviourThe following behaviors are enabled:When the dot_source is changed the graph will smoothly animate between the two states.You can click and drag the graph aroundYou can zoom in and out with the scroll wheelYou can reset the graph position and zoom by clicking the reset icon in teh top right cornerThe graph pane will size to whet ever it's parents size is, the graph will re-render but the re-render is debounced so as not to overload the browser.You can select a graph element (node or edge) which will fire an update to any dash callbacks that are connected.You can change the layout engine
Package: dash-io
dash-ioAn API prototype for simplifying IO in Dash. This is an experimental library and not an official Plotly product.QuickstartTo install the library:pipinstalldash-ioStart using it inside Pythonimportdash_ioasdio# ...url_df=dio.url_from_pandas(df)# dataframeurl_im=dio.url_from_pillow(im)# PIL image# ...df=dio.url_to_pandas(url_df)im=dio.url_to_pillow(url_im)UsagePillowfromPILimportImageimportnumpyasnpimportdash_ioasdio# Dummy image in Pillowim=Image.fromarray(np.random.randint(0,255,(100,100,3)))# Encode the image into a data urldata_url=dio.url_from_pillow(im,format="jpg")# Decode the data url into a PIL imageim=dio.url_to_pillow(data_url,format="jpg")The following format are currently supported:jpg,png.PandasIf you usexlsx, make sure to install a third-party engine such asopenpyxl.To use it in pandas:importpandasaspdimportdash_ioasdio# Dummy datadata={'col_1':[3,2,1,0],'col_2':['a','b','c','d']}df=pd.DataFrame.from_dict(data)# To encode/decode in binary CSV formatencoded=dio.url_from_pandas(df,format="csv",index=False)decoded=dio.url_to_pandas(encoded,format="csv")# To encode/decode in binary parquet formatencoded=dio.url_from_pandas(df,format="parquet")decoded=dio.url_to_pandas(encoded,format="parquet")# To encode/decode in string CSV format (i.e. text/csv MIME type)encoded=dio.url_from_pandas(df,format="csv",mime_type="text",mime_subtype="csv",index=False)decoded=dio.url_to_pandas(encoded,format="csv")The following format are currently supported:csv,parquet,feather,xlsx.JSONimportdash_ioasdio# Encode/decode dictionarydata={'col_1':[3,2,1,0],'col_2':['a','b','c','d']}encoded=dio.url_from_json(data)decoded=dio.url_to_json(encoded)# It also works with lists and other JSON-serializable objectsencoded=dio.url_from_json([1,2,3,4,5])Note that if adictkey is an integer, it will be converted to string byjson. This is a normal behavior.NumpyBy default,numpyarrays will not contain the mime header. However, you can enable it withheader=True(e.g. if you want to upload/download anpyfile).importdash_ioasdio# Encode/decode numpy arrays without MIME header by defaultarray=np.array([[1,2,3],[4,5,6]])encoded=dio.url_from_numpy(array)decoded=dio.url_to_numpy(encoded)# You can also use headersencoded=dio.url_from_numpy(array,header=True)decoded=dio.url_to_numpy(encoded,header=True)Note that pickling is disabled fornpyfiles for security reasons.DocumentationYou can access the documentation by calling:importdash_ioasdiohelp(dio)You can find the up-to-date output fromhelpinsideDOCS.txt.DevelopmentFirst, clone this repo:gitclonehttps://github.com/plotly/dash-ioTestingCreate a venv:python-mvenvvenvsourcevenv/bin/activateInstall dev dependencies:cddash-io
pipinstallrequirements-dev.txtRun pytest:python -m pytest
Package: dashiqlib
DashIQ LibThis repository is hosts DashIQ's python library.Running tests locallyClone the repoCreate a new virtual environmentInstall with the extra test dependencies w/pip install -e .[tests](note, if you use zsh you will have to escape the brackets i.e.\[tests\])Run the tests:pytest --cov --cov-report term-missing:skip-covered. Alternatively runmake tests
Package: dashit
Installation:pip install git+https://github.com/sede-x/dashit.git --upgradeRun:python examples/run.py -mtested withhydra-core==1.3.2, omegaconf==2.3.0, submitit==1.5.1, Python 3.11.3Tutorial:https://github.com/sede-x/playbooks/blob/main/submitit/tutorial.ipynbToDo:code checking with blackwriteup/examples to README
Package: dashjson
dashjson.py -- a Datadog toolA tool for exporting (or importing) Datadog dashboards to (or from) json. It allows the Datadog dashboard definitions to beversion controlled as json files, andmigrated across Datadog accountsTested with Python 3.6.7 and datadog 0.26.0.How to useCreate the credentials file containing the api_key and the app_key. Example content of the credentials file (your keys can be found athttps://app.datadoghq.com/account/settings#api):# cat ~/.dashjson.json
{
    "api_key": "abcdefg12345678",
    "app_key": "abcdefg987654321"
}Install via pip:python3 -m pip install --user dashjsonShow usage of the tool:python3 -m dashjson -hHow to build (for contributors only)These are the steps I used on Ubuntu 18.04.Add the following lines to~/.bashrc:# Due to https://github.com/pypa/pipenv/issues/2122
export PATH="${HOME}/.local/bin:$PATH"

# Due to https://github.com/pypa/pipenv/issues/1382
export PIPENV_VENV_IN_PROJECT=trueInstall pip and pipenv:sudo apt install python3-pip
python3 -m pip install --user pipenvClone and set up the project locally:git clone git@github.com:ouyi/dashjson.git
cd dashjson
pipenv install --dev
pipenv run python -m unittest
pipenv run python dashjson.py -hUpload a release:rm -rf build/ dist/
python setup.py sdist bdist_wheel
pip install . -vvv
python -m dashjson -h
vim setup.py # bump version
git add setup.py && git commit -m "Bump version"
git tag -a my_tag -m "My message"
git push origin master && git push origin --tags
python -m twine upload dist/*TODOsAutomatically determine the dashboard typeFor import, the information is available in the input file. For export, we can firstget a list of all screenboardsand then check existence by id.Embed a code coverage badgeThe coverage data can be generated with:coverage run --source=dashjson -m unittest && coverage report && coverage-badge -f -o coverage.svgAutomate the release process
Package: dash-kbar
dash kbarcmd + k interface for dash applicationsInstallationpipinstalldash_kbarUsagedash_kbar.DashKbar(id="input",actions=[{"name":"Action 1","id":"action1"},{"name":"Action 2","id":"action2"},],style={"itemTextColor":"grey",},)Look intousage.pyfor a functioning example usage.APIThe following properties are accepted byDashKbar.PropertyDescriptionTypeDefaultidThe ID used to identify this component in Dash callbacksstrNoneactionsList of actions to display in the kbarlist[dict[str, str]][]styleStyle of the kbardict[str, str]{}selectedID of the selected item. You can use this prop in callbacks.strNonedebugShow the id of the selected item in the browser consoleboolFalseActionsActions should be a list of dictionaries with the following keys.KeyDescriptionTypeRequirednameName of the actionstrYesidID of the actionstrYessubtitleSubtitle of the actionstrNokeywordsKeywords of the actionlist[str]NosectionSection of the actionstrNoiconIcon of the actionstrNoparentIf this is a child item, give the id of the parentstrNoactionableBoolean denoting whether selecting this action will change theselectedproperty. Set this toFalsefor parent actions where selecting the parent should show the child actions.boolNoshortcutShortcut of the actionlist[str]NoStylingStyle should be a dictionary with one or more of the following keys.KeyDescriptionDefaultmaxWidthMaximum width of the kbar600pxwidthWidth of the kbar100%backgroundBackground color of the kbarwhitesearchBackgroundBackground color of the search input boxtransparentsearchTextColorText color of the search input boxgreyfontFamilyFont family of the kbarsans-serifitemTextColorText color of the itemsgreyitemSubtitleTextColorText color of the item subtitlesgreysectionTitleTextColorText color of the section titlesgreyselectedBackgroundBackground color of the selected itemrgba(34, 139, 230, 0.1)selectedTextColorText color of the selected itemgreyselectedLeftBorderColorLeft border color of the selected itemrgba(34, 139, 230, 1)boxShadowBox shadow of the kbar0 0 20px rgba(0, 0, 0, 0.1)borderRadiusBorder radius of the kbar8pxsearchBoxFontSizeFont size of the search input box16pxresultFontSizeFont size of the items14pxsubtitleFontSizeFont size of the item subtitles12pxsectionTitleFontSizeFont size of the section titles10px
Package: dash-keycloak
This purpose of this library is to provide seamless integration of Plotly Dash 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 client for the application. Set "Access Type" to "confidential", set the valid redirect URIs (mandatory), and click "Save". Go to "Installation", select "Keycloak OIDC JSON" as "Format Option" and download the file.InstallationTo run the code, a virtual environment should be setup,python3 -m venv venvAfter activating the environment,source venv/bin/activateThe relevant packages can be installed aspip install -r requirements.txtRunning the exampleAfter completing the above step, the example can be run with the commandpython3 -m flask_keycloak.examples.dash_example /path/to/keycloak.jsonDeploymentBump the version number in setup.py and runpython3 setup.py sdist
pip3 install twine
twine upload dist/*
Package: dash-keycloak-2
This purpose of this library is to provide seamless integration of Plotly Dash 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 client for the application. Set "Access Type" to "confidential", set the valid redirect URIs (mandatory), and click "Save". Go to "Installation", select "Keycloak OIDC JSON" as "Format Option" and download the file.InstallationTo run the code, a virtual environment should be setup,python3 -m venv venvAfter activating the environment,source venv/bin/activateThe relevant packages can be installed aspip install -r requirements.txtRunning the exampleAfter completing the above step, the example can be run with the commandpython3 -m flask_keycloak.examples.dash_example /path/to/keycloak.jsonDeploymentBump the version number in setup.py and runpython3 setup.py sdist
pip3 install twine
twine upload dist/*
Package: dash-labs
Dash Labs tech previewThis repository contains a work-in-progress technical preview of potential future Dash features.🚧 Dash Labs features are not guaranteed to land in the officialdashpackage. These features are also not officially supported by Plotly's Support Team or by Dash Enterprise. We recommend waiting for these features to land indashbefore using them in a Production Environment. 🚧DocumentationThe documentation for Dash Labs can be found in thedocs/directory.01-Overview.mdArchived in dash-labs v0.4.0:02-CallbackEnhancements.md03-TemplateLayoutSystem.md04-PredefinedTemplates.md05-ComponentPlugingPattern.md06-TemplateIntegrationAndMigration.md07-LongCallback.mdMulti-Page App DocsNew in dash-labs>=1.0.0:08-MultiPageDashApp.mdOverview, quickstart apps, reference for thepagesplug-in09-MultiPageDashApp-NestedFolders.mdExample of organizing app pages in nested folders within thepages/folder10-MultiPageDashApp-MetaTags.mdDetails on how the title, image, and description are used to create the meta tags which determine how your app looks when shared on social media.11-MultiPageDashApp-LayoutFunctions.mdExamples of when to make thelayouta function - like when creating a custom menu by page, or using query strings.Multi-Page App DemosExamples and demos are located in thedocs/demosdirectory.multi_page_basicsMinimal examples of all the features and basic quickstart apps. (see chapter 8 for details.)multi_page_example1A quickstart app usingdash-bootstrap-componentsand some simple callbacks.multi_page_layout_functionsAn app that creates a sidebar menu for certain pages. (See chapter 11 for details.)multi_page_long_callbackAn example of how to use@app.long_callback()withpages/multi_page_meta_tagsThe example app used to show how the meta tags are generated. (See chapter 10 for details.)multi_page_nested_foldersThis is the example app used in chapter 9.multi_page_query_stringsAn example of using query strings in the URL to pass parameters from one page to another.InstallationTo install the latest version of dash-labs:$ pip install -U dash-labsTo install the archived version:$ pip install dash-labs==0.4.0We also recommend installing Pandas, which is required by Plotly Express and used in many of our examples.
Package: dashlang
dashlangAn experimental markup language for creating rich dashboards using Dash and Dash components.IntroductionDashlang is a project that offers developers a set of tools to easily create complex layouts
ofDashcomponents, using a syntax as close as possible to regular HTML. Its most simple usage
allows developers to create layouts using the dashlang declarative markup format and convert
the markup into a Python tree of Dash components in a simple and efficient way.ExampleTake the following snippet written in the dashlang markup format. The format tries to stay as
close to HTML as possible, by using tags delimited by the<and>characters, and using opening
and closing tags to declare elements.<Div id="root-container" className="fluid-container">
  <H1 id="title" className="text-bold">This is a title</H1>
  <P>This is a simple paragraph with a very very long text</P>
</Div>This snippet can be easily converted to a Python component tree just by calling a functionfrom dash import Dash
from dashlang.parsers import MarkupParser

parser = MarkupParser()

app = Dash(__name__)

with open("snippet.dml") as fp:
    app.layout = parser.parse(fp.read()).to_layout()

if __name__ == "__main__":
    app.run_server(debug=True)Running the previous Python snippet will create a Dash application instance and load its layout from
the snippet file containing the layout in markup format.
Package: dash-leaflet
Dash LeafletDash Leaflet is a light wrapper around React-Leaflet. The syntax is similar to other Dash components, with naming conventions following the React-Leaflet API.Getting startedThe easiest way to get started is to install the latest version of Dash and Dash Leaflet via pip.pip install dash
pip install dash-leafletOnce the installation is completed, paste the following lines of code into a .py file and run it.from dash import Dash
import dash_leaflet as dl

app = Dash()
app.layout = dl.Map(dl.TileLayer(), style={'height': '50vh'}, center=[56, 10], zoom=6)

if __name__ == '__main__':
    app.run_server()That's it! If you visithttp://127.0.0.1:8050/in your browser, you should see a Leaflet map.DocumentationThe documentation has been moved toa separate pageto enable an interactive example gallery.NB: The 1.0.0 release contains a wide range of breaking changes, please refer to the migration guidelinesSupportPlease ask questions onStackOverflowusing thedash-leaflettag, use GitHub issues only for bug reports.Build instructionsStart by cloning this repository,git clone git@github.com:thedirtyfew/dash-leaflet.git
cd dash-leafletNext, create a virtual environment and install the python dependencies,python3 -m venv venv && . venv/bin/activate
pip install -r requirements.txtFinally, install packages via npm (ignore errors) and run the build script,npm i --ignore-scripts
npm run buildDonation
Package: dashlog
DashlogOfficial wrapper over thedashlog.appapipipinstalldashlogfromdashlogimportDashlogdash=Dashlog('<YOUR-API-KEY>')response=dash.log(project='MyProject',# string - requiredchannel='Users',# string - requiredtitle='New User',# string - requireddescription='John Doe created an account',# string - optionnaldata={# dict[str, str | int | float | boolean] - optionnal'email':'john.doe@dashlog.app'# Values can be'username':'@johndoe',# strings,'age':29,# numbers,'premium':True# or booleans}notify=True,# boolean - optionnal - send log by email if True)print(response.status)# 200print(response.json())# {'status': 200, 'message': 'log added successfully'}
Package: dash-lumino-components
Dash Lumino ComponentsThis package integratesLumino Widgets, the basis ofJupyterLab, intoPlotly's Dash.Create a multi-window dash app with just a few lines of code.
Check out theexamples:dlc.MenuBar(menus,id="main-menu"),dlc.BoxPanel([dlc.SplitPanel([dlc.TabPanel([gapminderPlotsPanel,irisPlotsPanel,tipsPlotsPanel],id='tab-panel-left'),dlc.DockPanel([],id="dock-panel")],id="split-panel")],id="box-panel",addToDom=True)Local DevelopementInstall 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.txt
$ pip install -r tests/requirements.txtBuild your code$ npm run build
Package: dash.ly
## Testing```$ ag -l --python | entr python -m unittest discover -s tests/```***## Dash python backendThe backend doesn't serve files (yet), it only responds to HTTP calls from the web cloent. See [main README.md](../README.md) for instructions on running a local development web server.### running a python example```(dash2) $ cd dash(dash2/dash) $ pip install -r requirements.txtCollecting click==6.6 (from -r requirements.txt (line 1))Collecting Flask==0.11 (from -r requirements.txt (line 2))...Successfully installed Flask-0.11 Flask-Cors-2.1.2 Jinja2-2.8 MarkupSafe-0.23 Werkzeug-0.11.10 click-6.6 itsdangerous-0.24 numpy-1.11.0 pandas-0.18.1 plotly-1.11.0 python-dateutil-2.5.3 pytz-2016.4 requests-2.10.0 six-1.10.0(dash2/dash) $ python helloworld.py* Running on http://127.0.0.1:8050/ (Press CTRL+C to quit)* Debugger is active!* Debugger pin code: 254-379-279```### Python Developer guide#### Use `virtualenv`Running everything in the same virtual environment ensures that you don't haveconflicts with other globally installed python packages, and in general makesfor a much more consistent development environment. You could also createmultiple virtualenvs for different versions of Dash, for instance.##### Install the goods```sh$ pip install virtualenv$ pip install virtualenvwrapper```##### Add to your shell's configuration```sh# The actual file to edit will vary depending# on your OS, shell, and setup.$ vi ~/.bash_profile# Add these linesexport WORKON_HOME=$HOME/.py_virtualenvssource /usr/local/bin/virtualenvwrapper.sh```##### Create a virtual environment`my-dash-env` is a name of your choosing.```sh$ mkvirtualenv my-dash-env```##### Now you can switch to this virtual environment:```sh$ workon my-dash-env```More reading- http://docs.python-guide.org/en/latest/dev/virtualenvs/#### DebuggingInstall `ipdb`:```sh$ pip install ipdb```Insert a breakpoint in your Python code:```pyimport ipdb; ipdb.set_trace()```More reading- Intro: https://www.safaribooksonline.com/blog/2014/11/18/intro-python-debugger/- Cheat sheet: http://georgejhunt.com/olpc/pydebug/pydebug/ipdb.html- API: https://pypi.python.org/pypi/ipdb
Package: dash-mantine-components
Dash Mantine Components is an extensive (70+) Dash components library based onMantineReact Components Library. It makes it easier to create good quality dashboards with very well designed components out of the box.DocumentationTable of contentsInstallationQuickstartSponsorsContributingInstallationpipinstalldash-mantine-componentsQuickstartfromdatetimeimportdatefromdashimportDash,Input,Output,callback,htmlfromdash.exceptionsimportPreventUpdateimportdash_mantine_componentsasdmcapp=Dash(__name__)app.layout=html.Div([dmc.DatePicker(id="date-picker",label="Start Date",description="You can also provide a description",minDate=date(2020,8,5),value=None,style={"width":200},),dmc.Space(h=10),dmc.Text(id="selected-date"),])@callback(Output("selected-date","children"),Input("date-picker","value"))defupdate_output(d):prefix="You have selected: "ifd:returnprefix+delse:raisePreventUpdateif__name__=="__main__":app.run_server(debug=True)SponsorsThanks to the following people for supporting my efforts on dash-mantine-components.ASCEND.IOAnn Marie WardRick Ahlfjosca42ContributingJoin ourDiscordcommunity.Install virtual environment:python-mvenvvenvsourcevenv/bin/activate
pipinstall-rrequirements.txtInstall npm dependenciesnpminstallAdd your new component insrc/lib/components. Make sure to include it in thesrc/lib/index.jsas well.Build the components with the command:npm run build.Raise a PR, including an example to reproduce the changes contributed by the PR.
Package: dashmat
This is a dashboard framework written in Python.Documentation can be found athttp://dashmat.readthedocs.orgInstallationTo install dashmat, just create a virtualenv somewhere and:$pipinstalldashmatOr if you’re developing:$pipinstall-e.$pipinstall-e".[tests]"Then something like:$exportDASHMAT_CONFIG=dashmat.yml.example$dashmatrun_checks$dashmatserve
Package: dash-mediatools
DASH Industry Forum collection of various tools. A lot of them are small and
need to be run directly from the source tree.Major toolsdash-batch-encoder(dash_tools.batch_encoder)Usesffmpegto create multi-variant mp4 content with fixed
GoP durationOutput is suitable for transforming into DASH ABR contentConfigured via JSON recipesdash-ondemand-creator(dash_tools.ondemand_creator)UsesMP4Boxto transform the output of dash-batch-encoder into
DASH OnDemand content.Postprocesses audio tracks to get segment alignment with videoConfigured via JSON recipedash-ondemand-verifier(dash_tools.ondemand_verifier)Performs checks on (trees of) DASH OnDemand asset and reports issuesdash-livedownloader(dash_tools.livedownloader)Downloads a live DASH asset and stores on disk. Only supports
$Number$-templateThese above tools are exported as scripts starting with prefix dash-.
There corresponding names in the source code does not have that part.dash_tools.tsThis is a competent MPEG-2 TS parserFor more details, see onlinedocumentation.