Saravanan666/pypi-org huggingface dataset:
Package: jupyterlite-xeus-python
jupyterlite-xeus-pythonThexeus-pythonPython kernel for JupyterLite running in the browser.InstallYou can install the kernel with conda/mamba:mambainstall-cconda-forgejupyterlite-xeus-pythonOr using pip:pipinstalljupyterlite-xeus-pythonThen build your JupyterLite site:jupyterlitebuildPre-installed packagesxeus-python allows you to pre-install packages in the Python runtime. You can pre-install packages by adding anenvironment.ymlfile in the JupyterLite build directory, this file will be found automatically by xeus-python which will pre-build the environment when runningjupyter lite build.Furthermore, this automatically installs any labextension that it founds, for example installing ipyleaflet will make ipyleaflet work without the need to manually install the jupyter-leaflet labextension.Say you want to installNumPy,Matplotlibandipycanvas, it can be done by creating theenvironment.ymlfile with the following content:name: xeus-python-kernel
channels:
  - https://repo.mamba.pm/emscripten-forge
  - conda-forge
dependencies:
  - numpy
  - matplotlib
  - ipycanvasThen you only need to build JupyterLite:jupyterlitebuildYou can also pick another name for that environment file (e.g.custom.yml), by doing so, you will need to specify that name to xeus-python:jupyterlitebuild--XeusPythonEnv.environment_file=custom.ymlContributingDevelopment 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 jupyterlite-xeus-python directory# Install package in development modepython-mpipinstall-e".[dev]"# 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).Development uninstallpipuninstalljupyterlite-xeus-pythonIn 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 namedjupyterlite-xeus-pythonwithin that folder.Packaging the extensionSeeRELEASE
Package: jupyterlite-xeus-sqlite
jupyterlite-xeus-sqlite-kernelA SQLite kernel for JupyterLite running in the browser.RequirementsJupyterLite >= 0.1.0a14InstallTo install the extension, execute:pipinstalljupyterlite_xeus_sqliteThen build your JupyterLite site:jupyterlitebuildUninstallTo remove the extension, execute:pipuninstalljupyterlite_xeus_sqliteContributingDevelopment 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 jupyterlite_xeus_sqlite directory# Install package in development modepython-mpipinstall-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).Development uninstallpipuninstalljupyterlite_xeus_sqliteIn 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 namedjupyterlite-xeus-sqlitewithin that folder.Packaging the extensionSeeRELEASE
Package: jupyterlite-xeus-wren
jupyterlite-xeus-wren-kernelA wren kernel for JupyterLite running in the browser, powered by Xeus.RequirementsJupyterLite >= 0.1.0a14InstallTo install the extension, execute:pipinstalljupyterlite_xeus_wrenThen build your JupyterLite site:jupyterlitebuildUninstallTo remove the extension, execute:pipuninstalljupyterlite_xeus_wrenContributingDevelopment 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 jupyterlite_xeus_wren directory# Install package in development modepython-mpipinstall-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).Development uninstallpipuninstalljupyterlite_xeus_wrenIn 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 namedjupyterlite-xeus-wrenwithin that folder.Packaging the extensionSeeRELEASE
Package: jupyter-live-magic
# Jupyter Live MagicA Jupyter Notebook %%magic for periodic auto re-run and refresh of CellsUsageLoad extension inside a Jupyter notebook:` %load_ext jupyterlivemagic `Add code with Cell magic:` %%live # code tore-run`ExamplesDisplay current time, update every second:` %%live %%bash date `Display current datetime, update every 500ms:` %%live--refresh-rate500 import datetimestr(datetime.datetime.now())`InstallationInstall and activate dependencies:[ipywidgets](https://github.com/jupyter-widgets/ipywidgets)[jupyter-interval-widget](https://github.com/srizzo/jupyter-interval-widget)Then:$ pip install jupyter-live-magic
Package: jupyterlmod
Jupyter LmodJupyter interactive notebook server extension that allows user
to interact with environment modules before launching kernels.
The extension use Lmod's Python interface to accomplish module
related task like loading, unloading, saving collection, etc.requirementsjupyter notebook>= 6.0Lmod>= 6.0optional:jupyterlab>= 3.0optional:jupyter-server-proxy>= 3.2.0optional:jupyterlab-server-proxy>= 3.2.0If jupyter-server-proxy and jupyterlab-server-proxy are detected, jupyter-lmod will add the
proxy server launchers to JupyterLab UI when modules with matching names are loaded.setupinstallpip install jupyterlmodDisable jupyter-server-proxy notebook and lab extensionsTo avoid having items in the launcher that cannot be launched because the binaries location are not in PATH,
jupyter-lmod hides launcher items that do not have a corresponding loaded module.
jupyter-server-proxy notebook and lab extension always display the launcher item.
To avoid a situation where an item would be shown twice, we recommend disabling jupyter-server-proxy
notebook and lab extensions.This can be done with the following command for notebook:jupyter nbextension disable --py jupyter_server_proxy --sys-prefixand with the following commands for jupyterlab:jupyter labextension disable @jupyterlab/server-proxy
jupyter labextension disable jupyterlab-server-proxyPinning launcher itemsIf server proxies do not have a corresponding modules, or you wish to have their launcher items
displayed regardless of the loaded modules, you can define a list of items that will be pinned in
the Jupyter notebook configuration file, like this:c.Lmod.launcher_pins = ['Desktop', 'RStudio']demodeveloprequirementspip >= 23buildnodejs >= 18.xbuildwheel and tarball:pyproject-buildlabextensioncdjupyterlab
npminstall
npmrunbuild# To install extension in jupyterlab in develop mode:npmruninstall:extension
Package: jupyter-locuszoom
jupyter-locuszoomJupyter Widget for LocusZoomUsageImportLocusZoomin a notebook cell:# should it be renamed to ipylocuszoom? Let me know!fromjupyter_locuszoomimportLocusZoomand then use it with a GWAS result DataFrame which has to include columnsCHROM,REF,ALT,POS,P(orLOG10_P):LocusZoom(assoc,chrom='15',# optional (if not given top locus will be shown)start=0,# optional (if nto given top locus on given chromosome will be shown)end=100_000,# optional, but has to be given if start was givenbuild='GRCh38')Alternatively, center on a specific position:LocusZoom(assoc,chrom='15',position=123_456,flank=100_000,build='GRCh38')InstallationYou can install usingpip:pipinstalljupyter_locuszoomIf you are using Jupyter Notebook 5.2 or earlier, you may also need to enable
the nbextension:jupyternbextensionenable--py[--sys-prefix|--user|--system]jupyter_locuszoomDevelopment InstallationCreate a dev environment:condacreate-njupyter_locuszoom-dev-cconda-forgenodejsyarnpythonjupyterlab
condaactivatejupyter_locuszoom-devInstall the python. This will also build the TS package.pipinstall-e".[test, examples]"When developing your extensions, you need to manually enable your extensions with the
notebook / lab frontend. For lab, this is done by the command:jupyter labextension develop --overwrite .
yarn run buildFor classic notebook, you need to run:jupyter nbextension install --sys-prefix --symlink --overwrite --py jupyter_locuszoom
jupyter nbextension enable --sys-prefix --py jupyter_locuszoomNote that the--symlinkflag doesn't work on Windows, so you will here have to run
theinstallcommand every time that you rebuild your extension. For certain installations
you might also need another flag instead of--sys-prefix, but we won't cover the meaning
of those flags here.How to see your changesTypescript:If you use JupyterLab to develop then you 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 widget.# Watch the source directory in one terminal, automatically rebuilding when neededyarnrunwatch# Run JupyterLab in another terminaljupyterlabAfter a change wait for the build to finish and then refresh your browser and the changes should take effect.Python:If you make a change to the python code then you will need to restart the notebook kernel to have it take effect.Updating the versionTo update the version, install tbump and use it to bump the version.
By default it will also create a tag.pipinstalltbump
tbump<new-version>
Package: jupyter-manim
Integratesmanim(animation engine
for explanatory math videos) with Jupyter displaying the resulting video
when using%%manimcell magic to wrap a scene definition.Quick previewThe code in the example above comes from the excellentmanim
tutorial.Run a live demo in your browserby clicking
here.Installationpip3installjupyter-manimUsageTo enable the manim magic please runimport jupyter_manimfirst.
Then, you can use the magic as if it was the manim command: your
arguments will be passed to manim, exactly as if these were command line
options.For example, to render scene defined with classShapes(Scene)use%%manimShapesfrommanimlib.scene.sceneimportScenefrommanimlib.mobject.geometryimportCirclefrommanimlib.animation.creationimportShowCreationclassShapes(Scene):defconstruct(self):circle=Circle()self.play(ShowCreation(circle))Since version 1.0, the code is no longer required to be self-contained -jupyter_manimwill attempt to export your variables (and imported
objects) from the notebook into the manim script.Most variables can be easily exported, however there are limitations; in
shorteverything which can be
pickledcan be exported. Additionally, variables whose names start with an
underscore will be ommited.To display manim help and options use:%%manim -h
passThe%%manimmagic (by default) hides the progress bars as well as
other logging messages generated by manim. You can disable this
behaviour using--verboseflagIn the latest version of manimlib you can import everything at once
using:frommanimlib.importsimport*Video player control options--no-controls- hides the controls--no-autoplay- disables the autoplay feature-ror--resolution- control the height and width of the
video player; this option is shared with manim and requires the
resolution in following format:height,width,
e.g.%%manim Shapes-r200,1000--base64send the video with adata:URL instead of a local
path - useful for remote notebooks like Google Colab, or to embed the
video in notebook (note: the notebook size may increase rapidly)Compatibility and testingThis package is continuously tested with Python 3.7 on Ubuntu, Mac OS an
Windows.Tests have to be run with ipython, as the magic relies on IPython
instance being available:python3setup.pyinstallipython-mpytest----cov=.
Package: jupyter-markdown
🪐 📖 Jupyter MardownCreate code-interactive websites from your Jupyter and ObservableHQ notebooks.pipinstall-e.[test]jupyterlabextensiondevelop.--overwrite
jupyterlabextensionlist
jupyterserverextensionlist# open http://localhost:8686/api/jupyter/lab?token=60c1661cc408f978c309d04157af55c9588ff9557c9380e4fb50785750703da6yarnjupyterlabSeehttps://github.com/rafgraph/spa-github-pageshttps://github.com/fastai/fastpagesDocumentationJupyter Mardownhttps://jupyter-react/...🚧 Static ExampleYou can build a static example website that will be available under theexample/.outfolder.# This will open example/.out/index.html in your browser.makeexample-example&&\openexample/.out/index.htmlYou can deploy the example in yourVercel(former Now.js) account. Ccnfigure a verceldatalayer-jupyter-react-exampleproject with:Build command:yarn build:vercelOutput directory:storybook/.out.Then run the following command to deploy in you vercel.# Deploy the example (if you have karma for).# open https://api/jupyter.datalayer.iomakeexample-deploy
Package: jupyter-mdk
Jupyter Model Development Kit (MDK)The Jupyter MDK will allow a dramatic expansion of the capabilities of interactive View creation, by allowing seamless integration with theMMSalong with leveraging collaborative Jupyter sessions.It’s the vision that by utilising MMS’s element based storage and versioning scheme with Jupyter notebooks, and Python REST client libraries that can be used within a notebook, they can provide a more interactive and powerful way to construct Views while keeping the narrative oriented presentation of View Editor.QuickstartPrerequisitesJupyterLaborJupyter NotebookWill be automatically installed below if not already available.pipModel Management System (MMS)InstallationInstall the latestjupyter-mdk PyPi packageand its dependencies withpip install jupyter-mdkLocate yourJupyter config fileor generate one withjupyter notebook --generate-configthat defaults to~/.jupyter.Add configuration for MMS integration, e.g.frommmscontentsimportMMSContentsManagerc=get_config()# Tell Jupyter to use MMSContentsManager for all storage.c.NotebookApp.contents_manager_class=MMSContentsManagerc.MMSContentsManager.mms_url='https://mms.yourcompany.com'c.MMSContentsManager.mms_project='<project_id>'c.MMSContentsManager.mms_username='<username>'c.MMSContentsManager.mms_password='<password>'Run Jupyter as you normally would, e.g.jupyter notebookTest installation by creating a new notebook and saving. The notebook shouldnotbe in the local filesystem but instead accessible via MMS's REST API, e.g.https://mms.yourcompany.com/projects/<project_id>/refs/master/notebooks, to any user who has been granted access to the project both through MMS and Jupyter clients with Jupyter MDK similarly configured.
Package: jupyter-mindmaps
JupyterMind Maps of SpaceIntroductionUsing various APIs Jupyter collects JSON data and then uses Jinja2 templates to create markdown files. Using the markmap VS Code extension these markdown files render as mind maps!NASA API KeyPlease visitNASAAnd register for an API key. Once you have your key please add it to the .env fileSetupI recommend running Jupyter in a Python virtual environment. This will help keep your host system clean and allow you to have multiple environments to try new things. If you are not using a virtual environment, start at the download/clone step below.You will also need Python 3 and venv installed on your host system.In your project directory, create your virtual environmentpython3 -m venv envActivate (use) your new virtual environment (Linux):source env/bin/activatePip Install the package using pip install jupyter_mindmaps:(env)pip install jupyter_mindmapsMake a directory to hold your output(env)mkdir Space Mindmaps(env)cd Space MindmapsRun the code help(env)jupyter_mindmaps --helpRun the code passing the key(env)jupyter_mindmaps --token { your NASA token }Run the code interactively(env)jupyter_mindmapsNASA Token: { your NASA token }Run the code using an environment variable(end)export TOKEN={ your NASA token }(env)jupyter_mindmapsView the MindmapsType code . to launch VS Code in the folderInstall the markmap VS Code ExtensionOpen the markdown file and click the "Open as markmap"API ListThe following files are created from the various APIs. The Space.md file containsallAPIs.Original background photo creditPhoto
Package: jupytermodule
jupyter-moduleImport jupyter notebooks as python modules.Only imports, top-level variables, and class/function definition will be imported.Jupyter magics can still be used in the notebook without affecting importing.Installpip install jupytermoduleSynopsisfromjupytermoduleimportopennotebook_module=open("https://raw.githubusercontent.com/poga/jupytermodule/master/examples/primes.ipynb")print(notebook_module.primes(10))# [2,3,5,7]# Access to top-level variablesprint(notebook_module.PI)# 3.1415LicenseMIT
Package: jupyter-module-loader
Jupyter notebooks module loaderAllows to import Jupyter notebooks as if there were regular python modules.The code is based on the following example from Jupyter's documentation:https://jupyter-notebook.readthedocs.io/en/stable/examples/Notebook/Importing%20Notebooks.htmlInstallationpipinstalljupyter-module-loaderUsageSuppose a project has the following files structure:src/
├── main.py
└── my_notebook.ipynbLetmy_notebook.ipynbhave a cell containing following code:deffn():...Withinmain.pyit is possible to importmy_notebook.ipynblike so:importjupyter_module_loaderjupyter_module_loader.register()importmy_notebookmy_notebook.fn()# Calls fn() defined within the notebook above
Package: jupyter-msal-widget
jupyter_msal_widgetA Custom Jupyter Widget for MSAL authentication.InstallationYou can install usingpip:pipinstalljupyter_msal_widgetIf you are using Jupyter Notebook 5.2 or earlier, you may also need to enable
the nbextension:jupyternbextensionenable--py[--sys-prefix|--user|--system]jupyter_msal_widgetDevelopment InstallationCreate a dev environment:condacreate-njupyter_msal_widget-dev-cconda-forgenodejsyarnpythonjupyterlab
condaactivatejupyter_msal_widget-devInstall the python. This will also build the TS package.pipinstall-e".[test, examples]"When developing your extensions, you need to manually enable your extensions with the
notebook / lab frontend. For lab, this is done by the command:jupyter labextension develop --overwrite .
yarn run buildFor classic notebook, you need to run:jupyter nbextension install --sys-prefix --symlink --overwrite --py jupyter_msal_widget
jupyter nbextension enable --sys-prefix --py jupyter_msal_widgetNote that the--symlinkflag doesn't work on Windows, so you will here have to run
theinstallcommand every time that you rebuild your extension. For certain installations
you might also need another flag instead of--sys-prefix, but we won't cover the meaning
of those flags here.How to see your changesTypescript:If you use JupyterLab to develop then you 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 widget.# Watch the source directory in one terminal, automatically rebuilding when neededyarnrunwatch# Run JupyterLab in another terminaljupyterlabAfter a change wait for the build to finish and then refresh your browser and the changes should take effect.Python:If you make a change to the python code then you will need to restart the notebook kernel to have it take effect.DeploymentVersioningChange version in jupyter_msal_widget/_version.py (and package.json)Erase the /dist directory before each deployment proccessNPMThis requires a .npmrc at your user home directory with password/token//registry.npmjs.org/:_authToken=<token_for_npm># Erase the /dist directory if not empty# To generate the /dist foldernpmrunbuild:prod# Deploy it to NPMnpmpublishPip (testpypi & pypi)This requires a .pypirc at your user home directory with password/token for both repositories[pypi]
username = __token__
password = <token_for_pypi>

[testpypi]
username = __token__
password = <token_for_testpypi># Erase the /dist directory if not empty# If twine is not yet installedpipinstalltwine# To generate /dist folderpythonsetup.pysdist# Uploads the current /dist to testpypitwineupload--repositorytestpypidist/*# Test the uploaded componentpipinstall-ihttps://test.pypi.org/simple/jupyter-msal-widget# If everything looks fine, deploy to pipytwineupload--repositorypypidist/*
Package: jupyter-multiselection
MultiselectionThis nbextension allows to select occurences of the selected text in the currently active cell.InstallationFirst, installjupyter_multiselectionusing pip:pip install jupyter-multiselectionNext, install the nbextension using jupyter:jupyter nbextension install --py jupyter_multiselectionFinally, enable the nbextension:jupyter nbextension enable multiselection/multiselectionUsageSelect a word or section of text in edit mode and other occurences of the selection will be highlighted.Use the select next hotkey (defaultCtrl+m) to select the next occurence of the selection. To select all occurences use the select all hotkey (default: 'Ctrl+Alt+m').Optionshighlight- Iftrue, all occurences of the selected word will be highlighted (default:true).wrapcell- Iftrue, after the last occurence in the cell the next selection will start from the beginning (default:true).nextHotkey- Hotkey to select the next occurence (default:Ctrl+m).allHotkey- Hotkey to select all occurences (default:Ctrl+Alt+m).TODOMultiselection across all cellsSplit multi-line selection into multiple selectionsHistory@chdudek (Feb 29 2020): Inital release