Saravanan666/pypi-org huggingface dataset:
Package: jupyter-tigervnc-novnc-proxy
Jupyter TigerVNC/NOVNC proxyjupyter-tigervnc-novnc-proxy description (TODO)TODOsLook for TODOs in files:grep-rTODO.InstallationYou can install jupyter-pgadmin-proxy inside your environment with Jupyter / Jupyterlab:python3-mpipinstalljupyter-tigervnc-novnc-proxyBuildpython3-mpipinstallhatch

hatchbuild

ls-ladist/*DevelopmentTrymake helpto see available commands:makehelppython3-mpipinstallgit+https://github.com/huntdatacenter/jupyter-tigervnc-novnc-proxy.git@mainegg=jupyter-tigervnc-novnc-proxyTesting in dockerRun/rebuild local Jupyterlab service:makerebuildRunning the command should open a url in the browserhttp://127.0.0.1:8888/labTo stop the service run:makedown
Package: jupyter-tincan
Welcome to Jupyter-TinCan!In the realm of data science and software development, safeguarding sensitive information is paramount. Traditional approaches, such as using remote desktop interfaces to access Jupyter notebooks, offer security but at a cost to user experience. These methods typically involve a cumbersome 'browser within a browser' setup, leading to ergonomic challenges like conflicting keyboard shortcuts, noticeable latency, and a disconnect from local development tools like VSCode. Often, developers find themselves forced into less efficient workflows, such as committing code remotely instead of locally.Jupyter-TinCan changes the game by offering a simpler, more intuitive solution. It transforms sensitive text in notebook cells into images, maintaining data security while enhancing user experience. No more cumbersome setups or workflow disruptions – just smooth, secure, and efficient development.Installation$pipinstalljupyter-tincanYou also need nodejs and have text2svg available.$npminstall-gtext2svgUsageYou can configure any pre-existing Jupyter kernel to use Jupyter-TinCan. First let's list the kernels we have installed:$jupyterkernelspeclistAvailable kernels:python3    /usr/local/share/jupyter/kernels/python3Now let's put the python3 kernel into TinCan mode:$mkdirpython3-tincan$jupytertincancreate-kernel/usr/local/share/jupyter/kernels/python3>python3-tincan/kernel.jsonThis will create a new kernel spec file calledtincan-python3.jsonin the current directory. You can now install this kernel spec into Jupyter:$jupyterkernelspecinstallpython3-tincanor$jupyterkernelspecinstall--userpython3-tincanAcknowledgments"Jupyter" and the Jupyter logos are trademarks of the NumFOCUS foundation. Our use of these trademarks does not imply any endorsement by Project Jupyter or NumFOCUS. Jupyter-TinCan is an independent project developed to integrate with Jupyter software.This project is not affiliated with Project Jupyter but is designed to be compatible with and enhance the Jupyter notebook experience.DisclaimerJupyter-TinCan is experimental software provided 'as-is' without any express or implied warranties. By using this software, users acknowledge the potential risks, such as data loss or incorrect data transformations, and agree to bear full responsibility for any consequences arising from its use. The developers are not liable for any damages or losses incurred from the software's operation or failure.
Package: jupyter-toc
Jupyter Notebook ToCThis is a simple extension to generate a table of contents for a Jupyter notebook.Basic Usageimportjupyter_tocjupyter_toc.build()It will return something like this:Table of Contents-[Introduction](#introduction)-[Subhead](##subhead)-[Usage](#usage)-[Development](#development)Advanced UsageYou can pass arguments to customize the following:title- The title of the table of contents.depth- The maximum depth of headings to include in the table of contents.make_hyperlinks- Whether to make the table of contents entries hyperlinks.print_output- Whether to print the ToC or just return it as a string.ContributingFeel free to open an issue or submit a pull request.To publish a new version to PyPI:pipinstalltwinewheel# if you don't have it alreadypythonsetup.pysdistbdist_wheel# build the packagetwineuploaddist/*# upload to PyPIDevelopmentRunning TestsThe test suite can be run using Python's built-inunittestframework.Navigate to thetestsdirectory and run:python -m unittest test_jupyter_toc.pyOr, if you're in the root directory:python -m unittest tests/test_jupyter_toc.pyAuthorsOdai Athamneh
Package: jupyter-toc-generator
jupyter-toc-generatorGenerate a table of contents incl. links and anchor tags. Writes directly to the Jupyter Notebook.
Alternatively, copy the generated table of contents to clipboard.No third party dependencies except Pyperclip if using --clipboard option.Requires Python 3.7 or higher. Therefore, compatible with AWS Sagemaker.Usage Optionsgenerate_toc /home/user/notebook.ipynb [options]
    generate_toc "C:\My Files\My Notebook.ipynb" [options]
    generate_toc --help

    python generate_toc /home/user/notebook.ipynb [options]
    python generate_toc "C:\My Files\My Notebook.ipynb" [options]
    python generate_toc --helpOptionsParameterMandatoryDescriptionExamplexPath to Jupyter notebook (relative or absolute)/home/user/notebook.ipynp--log_level (-l)Set Log level (DEBUG, INFO, WARNING, ERROR, CRITICAL). Default: INFO.-l DEBUG--skip_anchor_tags (-sa)Don't add anchor tags to header cells if missing.-sa--skip_write_toc (-sw)Don't Update TOC directly in notebook or insert if missing.-sw--force_toc_in_first_cell (-f)If inserting missing TOC, don't skip overall title cell but always insert into first cell.-f--clipboard (-c)Copy generated TOC to clipboard.-cScreenshotsNotebook beforeNotebook after TOC generationGenerated Markdown Code for TOC and anchor tags added to headers
Package: jupyter-traitlets
Jupyter UI Traitlets🪐 ⚛️ Create UI for your Jupyter TraitsThis package eases the creation of user interfaces based on theJupyter Traitletsconfiguration system.This allows a developer to start from the traits (configuration definition) defined in Python code and automatically generate aReact.jsuser interface which can be used to manage your settings in a visual way. The traits would be converted tojson-schemadefinitions that can be used to create the React.js components.This is particularly useful to build management user interfaces.pipinstall-e.[test]jupyterlabextensiondevelop.--overwrite
jupyterlabextensionlist
jupyterserverextensionlist
yarnjupyterlab
Package: jupyter-trame-proxy
jupyter-trame-proxyIntegratestramein your Jupyter environment usingParaView Visualizer.RequirementsPython3.9+ParaView5.10+Jupyter Notebook 6.0+JupyterLab 2.1+This extension will start thepv_visualizerpackage. You must make sure it is on the PYTHONPATH! Adjustlaunch_trame.shto ensure this.InstallCreate and Activate Environmentvirtualenv -p python3 venv
source venv/bin/activateInstall jupyter-trame-proxypip install git+https://github.com/jwindgassen/jupyter-trame-proxy.gitEnable jupyter-trame-proxy ExtensionsFor Jupyter Classic, activate the jupyter-server-proxy extension:jupyter serverextension enable --sys-prefix jupyter_server_proxyFor Jupyter Lab, install the @jupyterlab/server-proxy extension:jupyter labextension install @jupyterlab/server-proxy
jupyter lab buildStartingClick on theParaView trameicon from the Jupyter Lab Launcher or theParaView trameitem from the new dropdown in Jupyter Classic.Jupyter-Server-ProxyJupyter-Server-Proxylets you run arbitrary external processes alongside your notebook and provide web access to them. After installing this proxy, you can start trame using theParaView trameentry in the launcher. It will start the server (which might take a few seconds) and open the website in a new tab.LicenseThis software is provided under the BSD 3-Clause
Package: jupytertweak
jupytertweakTable of ContentsInstallationUsageCommandsSupportContributingInstallationThejupytertweakgit repois available asPyPi packagepip install jupytertweakUsageCheck theexamplesfolder for notebooks.CommandsCheck syntax:flake8 --ignore=F401Remove.pycfiles:find . -type f -name "*.pyc" | xargs rmRemove__pycache__folders:find . -type d -name "__pycache__" | xargs rm -rfUpload to PyPi:python setup.py sdist upload -r pypiUpload to PyPi with twine:python setup.py sdist && twine upload -r pypi dist/*SupportPleaseopen an issuefor support.ContributingPlease contribute usingGithub Flow. Create a branch, add commits, andopen a pull request.
Package: jupyter-utility-widgets
python-utilitySeeing I have been using a few widgets "patterns" over and over, I have decided to share and pack them in a convenient manner.Installationpip install jupyter-utility-widgetsmay require jupyterlab installation.WidgetsIndex Selector
A simple combination of widgets that help exploring a list of options.seeexamples/index_selector.ipynbFile Selector
A combination of widgets that help exploring the file system
seeexamples/file_selector.ipynbSpectrogram Viewer
A class for viewing spectrogram of data, with a simultaneous zoomed view.
seeexamples/spectrogram.ipynb
Package: jupyter_utils
A set of Python utility methods to ease usage of Jupyter notebookFree software: MITSource:https://github.com/Stibbons/jupyter_utilsInstallationInstalljupyter_utilsin Anaconda:$sourceactivatemy_conda_env$pipinstalljupyter_utilsNote: only dependencies described inrequirements.txtwill be installed when usingpip install.
The development dependencies (pylint,…) andnotinstalled on deployment.Usage>From now, on every Jupyter notebook that use this conda environment, you can install any missing
anaconda package directly from the cell.Install Anaconda packageAn anaconda package can be installed directly from the notebook using! conda install …, but
you need to specify the name of the kernel. To simply this, Jupyter Utils provides:fromjupyter_utilsimportcondaconda.install("numpy")Grid Search CV on Apache Spark 1.6Easily distribute Scikit-learn Cross Validation on a Spark Cluster. Only for Spark 1.6.x. For Spark
2, useSparkit-LearnorSpark-SKLearn.fromjupyter_utils.sparkimportSparkGridSearchCVSparkGridSearchCV(sc,model,params)ContributingCreate a virtualenv:$virtualenvvenv$sourcevenv/bin/activate$pipinstall--upgradepip# Force upgrade to latest version of pipSetup for production:$pipinstall-rrequirements.txt.Setup for development and unit tests:$pipinstall--upgrade-rrequirements.txt-rrequirements-dev.txt-e.$pythonsetup.pydevelopExecute unit tests:$pythonsetup.pytestCode Style:$pythonsetup.pyflake8$yapf-r-ijupyter_utilsBuild:$# Source package$pythonsetup.pysdist$# Binary package:$pythonsetup.pybdistbdist_wheel
Package: jupyter-utils-tddschn
jupyter-utils-tddschnMost code were copied fromhttps://jupyter-notebook.readthedocs.io/en/stable/examples/Notebook/Importing%20Notebooks.htmlHow do I ... ?Import a.ipynbfile?fromjupyter_utils_tddschnimportnotebook_importer# then just import a .ipynb file like a .py file# to import test.ipynb:importtestShow the content of a notebook, syntax highlighted?fromjupyter_utils_tddschn.notebook_showerimportshow_notebookshow_notebook('notebooks/test.ipynb')
Package: jupyterview
jupyterviewVTK Data visualization extension for JupyterLabjupyterviewis an extension that adds theVTKdata visualization capability to JupyterLab.Powered by Kitware'svtk.jsanditk-wasmlibrary,jupyterviewis a pure frontend extension, it does not require any kernel to operate and fully supports the Real-Time Collaboration feature of JupyterLab.jupyterviewis fully compatible withjupyterlite, it is available online atjupyterview demo link.FeaturesVisualize structured and unstructured data (.vtu,.vtp,.vtkflies).Visualize and animate.pvdfiles.Visualize mesh supported bymeshiolibrary. Full list of supported formats is available athttps://github.com/nschloe/meshio. To openmedfiles,h5pyneed to be installed.https://user-images.githubusercontent.com/4451292/173418573-1839d689-763f-42ba-add3-4eaac4c87d7e.mp4Multiple display modes:Surface,Surface with Edge,WireframeandPointsColor scale and isocolor effet.Warp by scalar effet.RequirementsJupyterLab >= 3.0InstallTo install the extension, execute:pipinstalljupyterviewor with mamba/condacondainstall-cconda-forgejupyterviewContributingDevelopment installNote: You will need NodeJS to build the extension package.Thejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Change directory to the jupyterview directory# Install package in development modepipinstall-e.# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Rebuild extension Typescript source after making changesjlpmrunbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmrunwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, thejlpm run buildcommand generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseDevelopment uninstallpipuninstalljupyterviewIn development mode, you will also need to remove the symlink created byjupyter labextension developcommand. To find its location, you can runjupyter labextension listto figure out where thelabextensionsfolder is located. Then you can remove the symlink namedjupyterviewwithin that folder.Packaging the extensionSeeRELEASEThanksThe following libraries / open-source projects were used or inspired in the development of jupyterview:vtk.jsitk-wasmparaview-glance
Package: jupyter-viewer
🪐 👀 Jupyter ViewerA revisited NbViewer as a modern Web application to view Jupyter notebooks.NbVieweris built on top of theGitHub nbviewer repositoryand has been an attraction point for many data scientists since years.Datalayer Jupyter Viewer takes over the concept of easy visualisation and brings it to the Web application developers with React.js components.This repository packages theJupyter UI Viewercomponent as a Jupyter Server extension. Install and launch with the following commands (you will need Python installed on your machine).pipinstalljupyter_viewer# open http://localhost:8888/jupyter_viewer# python -m jupyter_viewerjupyterviewerAfter launching, enjoy the 👀 views.Developyarn
yarnbuild# open http://localhost:3063# open http://localhost:8686/api/jupyter/lab?token=60c1661cc408f978c309d04157af55c9588ff9557c9380e4fb50785750703da6yarnstartpipinstall-e.[test]jupyterlabextensiondevelop.--overwrite
jupyterlabextensionlist
jupyterserverextensionlist# open http://localhost:8686/api/jupyter/lab?token=60c1661cc408f978c309d04157af55c9588ff9557c9380e4fb50785750703da6yarnjupyterlabReleasesJupyter Viewer is released inPyPI.
Package: jupyter-viz-extension
jupyter_viz_extensionA JupyterLab extension.This extension is composed of a Python package namedjupyter_viz_extensionfor the server extension and a NPM package namedjupyter-viz-extensionfor the frontend extension.RequirementsJupyterLab >= 3.0InstallTo install the extension, execute:pipinstalljupyter_viz_extensionUninstallTo remove the extension, execute:pipuninstalljupyter_viz_extensionTroubleshootIf you are seeing the frontend extension, but it is not working, check
that the server extension is enabled:jupyterserverextensionlistIf the server extension is installed and enabled, but you are not seeing
the frontend extension, check the frontend extension is installed:jupyterlabextensionlistContributingDevelopment installNote: You will need NodeJS to build the extension package.Thejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Change directory to the jupyter_viz_extension directory# Install package in development modepipinstall-e"."# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Server extension must be manually installed in develop modejupyterserverextensionenablejupyter_viz_extension# Rebuild extension Typescript source after making changesjlpmbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, thejlpm buildcommand generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseDevelopment uninstall# Server extension must be manually disabled in develop modejupyterserverextensiondisablejupyter_viz_extension
pipuninstalljupyter_viz_extensionIn development mode, you will also need to remove the symlink created byjupyter labextension developcommand. To find its location, you can runjupyter labextension listto figure out where thelabextensionsfolder is located. Then you can remove the symlink namedjupyter-viz-extensionwithin that folder.
Package: jupyter-vscode-server
Jupyter VS Code ServerAbout jupyter-vscode-serverA Jupyter Notebook extension to launchcdr/code-server(VS Code).Using jupyter-vscode-serverYou must already have code-server installed. Check out code-server'sGetting Startedsection.Extension can be install with:pipinstalljupyter-vscode-serverExample Dockerfile segment to install code-server:ENVCODESERVER_URL="https://github.com/cdr/code-server/releases/download/1.1119-vsc1.33.1/code-server1.1119-vsc1.33.1-linux-x64.tar.gz"\CODESERVER="code-server1.1119-vsc1.33.1-linux-x64"RUNwget${CODESERVER_URL}&&\tarxvf${CODESERVER}.tar.gz&&\mv${CODESERVER}/code-server/usr/local/bin/&&\rm-rfcode-server*&&\rm-rf/tmp/*&&\rm-rf$HOME/.cache&&\rm-rf$HOME/.node-gyp&&\fix-permissions$CONDA_DIR&&\fix-permissions$HOME
Package: jupyter-weav-app-four
jupyter_weav_app_fourTest WEAV AIRequirementsJupyterLab >= 3.0InstallTo install the extension, execute:pipinstalljupyter_weav_app_fourUninstallTo remove the extension, execute:pipuninstalljupyter_weav_app_fourContributingDevelopment installNote: You will need NodeJS to build the extension package.Thejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Change directory to the jupyter_weav_app_four directory# Install package in development modepipinstall-e.# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Rebuild extension Typescript source after making changesjlpmrunbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmrunwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, thejlpm run buildcommand generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseDevelopment uninstallpipuninstalljupyter_weav_app_fourIn development mode, you will also need to remove the symlink created byjupyter labextension developcommand. To find its location, you can runjupyter labextension listto figure out where thelabextensionsfolder is located. Then you can remove the symlink namedjupyter_weav_app_fourwithin that folder.
Package: jupyter-weav-app-one
jupyter_weav_app_onetest weavRequirementsJupyterLab >= 3.0Installpipinstalljupyter_weav_app_oneContributingDevelopment installNote: You will need NodeJS to build the extension package.Thejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Change directory to the jupyter_weav_app_one directory# Install package in development modepipinstall-e.# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# If using a server extension, it must be manually installed in develop modejupyterserverextensionenable<extension_name># Rebuild extension Typescript source after making changesjlpmrunbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmrunwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, thejlpm run buildcommand generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseUninstallpipuninstalljupyter_weav_app_one