Saravanan666/pypi-org huggingface dataset:
Package: ipypixano
ipypixanoPixano Element in jupyter widgetInstallationYou can install usingpip:pipinstallipypixanoIf you are using Jupyter Notebook 5.2 or earlier, you may also need to enable
the nbextension:jupyternbextensionenable--py[--sys-prefix|--user|--system]ipypixanoDevelopment InstallationCreate a dev environment:condacreate-nipypixano-dev-cconda-forgenodejsyarnpythonjupyterlab
condaactivateipypixano-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 ipypixano
jupyter nbextension enable --sys-prefix --py ipypixanoNote 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.
Package: iPyPlayer
iPyPlayer============================================================Overview========================================================================================================================PyCharm를 이용한 파이썬64비트 가상환경 구성============================================================64비트 가상환경 구성
6.0 python3.9-64bit Installer 로 설치
6.1 PyChar실행
6.2 File / Settings / Python Interpreteripp_env_py39_64bit 폴더명 설정C:\Python39\python.exe 선택
cd "C:\pypjts\iPyPlayer"
6.3 .\ipp_env_py39_64bit\Scripts\activate
6.4 pip install --upgrade pip
6.5 pip install -r requirements.txt6.6 pip install jupyter
6.7 pip install ipykernel
6.8 python -m ipykernel install --name=env_py39_64bit
6.9 jupyter notebook
6.10 메뉴 / Kernel / Change kernel============================================================PyPi 패키지 업로드============================================================py -m pip install --upgrade buildpy -m buildpy -m pip install --upgrade twinesetup.py 파일내용변경py -m twine upload --repository pypi dist/*
Package: ipypm
ThepyPM.capopulation modeller (www.pyPM.ca) describes connected systems with
discrete-time difference equations. It was developed specifically
to understand and characterize the CoViD-19 epidemic.This package, ipypm, is a convenient graphical user interface (GUI) for
pyPM.ca based in interactive python widgets for use inside a Jupyter notebook.
After opening the GUI in a notebook, the user can open a pyPM.ca model
and compare its predictions with data.
Models and data can be accessed on the local filesystem or downloaded
from a network server.
The model parameters can be adjusted manually or by fitting to data.
Models can be examined in detail and modified through the GUI, without
programming.
Package: ipypolaroid
ipypolaroidA Custom Jupyter Widget LibraryInstallationYou can install usingpip:pipinstallipypolaroidIf you are using Jupyter Notebook 5.2 or earlier, you may also need to enable
the nbextension:jupyternbextensionenable--py[--sys-prefix|--user|--system]ipypolaroidDevelopment InstallationCreate a dev environment:condacreate-nipypolaroid-dev-cconda-forgenodejsyarnpythonjupyterlab
condaactivateipypolaroid-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 ipypolaroid
jupyter nbextension enable --sys-prefix --py ipypolaroidNote 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.
Package: ipyprogressbar
IPyProgressBarA small package that provides an python-asynchronous progressbar widgets for use in Jupyter/IPython in conjunction withipywidgets.WhyMany times, when building a small dashboard on IPython there are functions that take some time to be executed but cannot be split into chunks, in the middle of which we could update a progress bar.Since we want to give the user feedback that we are actually doing something and now he has to wait for a little, we can simply display the prototypicalLoading....However for some (most?) functions we may actually have a rough estimate of the time of wait and can make the wait a little more visual with a progressbar!"Python-Asynchronous"The progressbar needs to be updated (filled) while python is working on other things. Therefore, we can use a python thread to update the value of the progressbar. That way, our main python process can take care of other code meanwhile.Usage# Create the progress bar object (not the widget)my_progressbar=AsyncProgressBar(time=2,description='Loading dataset:')# Return the widget for displaymy_progressbar.display()# ...# Trigger the progressbarmy_progressbar.run()The object also has aclose()method (and its inversereopen()), for hiding the progressbar whenever we want:my_progressbar.run()function_that_takes_long()my_progressbar.close(on_finish=True)# or set close_on_finish=True on creating the progressbar objectHowever,for a typical use case in which we just want a temporal progressbar, we can specify theclose_on_finishon creation:# Create the progress bar object (not the widget)my_progressbar=AsyncProgressBar(time=2,description='Loading dataset:',close_on_finish=True)# ...# Trigger the progressbarmy_progressbar.run()# when done the progress bar will hideContactCreated by:WillahScott
Package: ipy-progressbar
Introductionipy-progressbar allows to use the same code for rich progressbars in
IPython Notebooks, and for simple fallback ones in terminal sessions.ExampleCode like this:fromipy_progressbarimportProgressBarpb=ProgressBar(10,title='Outer')pb_inner=ProgressBar(5,title='Inner')foriinpb:)forjinpb_inner:# inner loop bodywill work both in IPython Notebook and in plain console. When run in
notebook, the output will be rich (Bootstrap progress bars), while
console version is mainly thought of as a fallback.For more examples seeexample.pyandexample.ipynb.
Package: ipypublish
IPyPublishA program for creating and editing publication ready scientific reports and presentations,
from one or more Jupyter Notebooks.Documentation:ipypublish.readthedocs.ioAttention:
A major API improvement occurred inv0.7.0. This has not changed the
general user interface, but anyone using custom converter plugins will
be required to update them (seePlugin Conversion)For an example of the potential input/output, see:Example.ipynb,Example.pdf,Example.htmlandExample.slides.html.Or, for a practical example of the ipypublish capability, see these documents on Atomic 3D Visualisation:Notebook,PDF,HTMLorReveal.JS slideshow.Design PhilosophyIn essence, the dream is to have the ultimate hybrid of Jupyter Notebook, WYSIWYG editor (e.g. MS Word) and document preparation system (e.g.TexMaker), being able to:Dynamically (and reproducibly) explore data, run code and output the resultsDynamically edit and visualise the basic components of the document (text, math, figures, tables, references, citations, etc).Have precise control over what elements are output to the final document and how they are layed out and typeset.Also be able to output the same source document to different layouts and formats (pdf, html,presentation slides, etc).WorkflowCreate a notebook with some content!optionally create a .bib file and external imagesAdjust the notebook and cell metadata.install ipypublish and run thenbpublishfor either the specific notebook, or a folder containing multiple notebooks.A converted folder will be created, into which final.tex.pdfand.htmlfiles will be output, named by the notebook or folder inputThe default latex template outputs all markdown cells (unless taggedlatex_ignore), and then only code and output cells withlatex metadata tags.
SeeExample.ipynb,Example.pdf,Example.htmlandExample.slides.htmlfor examples of the potential input and output.See the project site for more info!AcknowledgementsIPyPublish is built as an extension tonbconvert.I also took strong influence from:Julius SchulzDan MackinlayNotebook concatination was adapted fromnbconvert issue#253
Package: ipypy
ipypyStore Jupyter Notebooks in a more repo and coding friendly way.Have you ever noticed that a Jupuyter Notebook nowadays is a json file? Containing together the metadata of the notebook itself, the list of cells, and for each cell, not only thesource(what was written on it), but also theoutputs, andmetadatafor each cell.This project attemps to propose an alternative for that.HowYou can pick between a SplitCodeManager, which stores each notebook in:my_file.ipynb (the usual notebook file, but with source information extracted)my_file.ipypy (a pure code file that stores only the actual source code)Or, a SplitOutputManager, which stores each notebook in:my_file.ipynb (the usual notebook file, but without the cells output)my_file.nbout (a json file that stores only the outputs of each cell)BenefitsYou can now import your notebook from another fileYou can now use standard coding tools and practices for manipulating Notebooks code:testingimport code defined in thereeditable naturally by any editorrefactoringtracking changes...The source code of your notebook, can be versioned in a repository, where diffs, and history is readable. Now makes sense. It's code.(Work In Progress) You can choose to simply ignore the metadata files (.ipynb) in the repository, or keep them versioned. It should be up to you.WarningWe are in beta. Once you open a notebook with this extension enabled, and later save it, your notebook will be saved in a format a bit incompatible.Installation$ pip install ipypyYou will also need to configure your jupyter so it usesipypy, by editing your jupyter config file, or from command line$ jupyter lab --NotebookApp.contents_manager_class="ipypy.SplitCodeManager"
Package: ipyquery
Linq for PythonWhat's thatSimpleLanguage integrated queryfor PythonThis package helps you write queries on Iterable objects in pythonIt makes it easy to deal with iterators.Why should i use this?Well you should not! i mean you don't need such thing at all. python has already fully implemented methods for iterables, but if you used toLinqinC#( like me ), you are probably want to use such package.How to?Well it's super easyLet's take a look at the examplesfromipyqueryimportLinqmy_list=Linq([5,1,7,2,3,10,1,4,5])powered_cleaned=my_list.distinct().where(lambdax:x<=5).orderby().select(lambdax:x**2).tolist()print(powered_cleaned)# [1, 4, 9, 16, 25]You can use methods to do whatever you want with your list just that easy!More Examples?Take a look attestsfolder ( There are a lot to explore )Currently available methodsBelow you can see all currently available methods, all of these can be used onLinqobject ( which contains your list )selectselect_manyenum_selecttolisttodictgroupbywherefirstfirst_or_defaultanyallorderbyorderby_descmaxminsumaveragedistinctsinglesingle_or_defaultaddreverseremoveremove_alltake(new)skip(new)Don't worry will add more methods.
Package: ipyradiant
ipyradiantJupyter WidgetsforRDFgraph interaction, querying, and visualization
inJupyterLab.InstallDemoBuildDocsCHANGELOGCONTRIBUTINGexamplesPowered byipyctoscape,datashader, andholoviews.Visualization Widgetsipyradiantincludes several widgets for visualizing and interacting with RDF graphs
that can be accessed through the examples.Example Tooling Widgetsipyradiantincludes examples where visualization and utility widgets are linked into
example tooling.Prerequisitespython >=3.6ipyradiant's python dependencies will install their requirednbextensionsfor
Notebook Classic.For JupyterLab support, ensure you have the following installed:jupyterlab >=3nodejs >=10JupyterLab compatibilityWhileipyradiantdoesn't provide any JupyterLab extensions, it depends on a number of
them.The release of JupyterLab 3 has made some version compatibility unpredictable. Below are
some researched combinations that should work.jupyterlabipycytoscapepyviz_commspip install>=3,<4>=1.1.0>=1.0.3ipyradiant[lab3]Installipyradiantwithconda(recommended)condainstall-cconda-forgeipyradiantipyradiantwithpippipinstallipyradiantFor additional information, seeCONTRIBUTING.mdOpen SourceThis work is licensed under theBSD-3-Clause License.
Package: ipyreactive
Reactive IPyWidgetsReactive IPyWidgetshttps://ipyreactive.datalayer.techfor the Datalayer literate editor.A Custom Jupyter Widget LibraryInstallationYou can install usingpip:pipinstallipyreactiveIf you are using Jupyter Notebook 5.2 or earlier, you may also need to enable
the nbextension:jupyternbextensionenable--py[--sys-prefix|--user|--system]ipyreactiveDevelopment InstallationCreate a dev environment:condacreate-nipyreactive-dev-cconda-forgenodejsyarnpythonjupyterlab
condaactivateipyreactive-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 ipyreactive
jupyter nbextension enable --sys-prefix --py ipyreactiveNote 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.
Package: ipyregulartable
Anipywidgetswrapper ofregular-tablefor Jupyter.ExamplesTwo Billion RowsNotebookClick EventsNotebookEdit EventsNotebookStylingNotebookPandas Data ModelFor interactive/streaming sorting/pivoting/aggregation, take a look atPerspective,Streaming pivot visualization via WebAssembly, which also leveragesregular-table.NotebookSeriesDataFrameDataFrame - Row PivotsDataFrame - Column PivotsDataFrame - Pivot TableInstallationYou can install usingpip:pipinstallipyregulartableOr if you use jupyterlab:pipinstallipyregulartable
jupyterlabextensioninstall@jupyter-widgets/jupyterlab-managerIf you are using Jupyter Notebook 5.2 or earlier, you may also need to enable
the nbextension:jupyternbextensionenable--py[--sys-prefix|--user|--system]ipyregulartableData ModelIt is very easy to construct a custom data model. Just implement the abstract methods on the baseDataModelclass.classDataModel(with_metaclass(ABCMeta)):@abstractmethoddefeditable(self,x,y):'''Given an (x,y) coordinate, return if its editable or not'''@abstractmethoddefrows(self):'''return total number of rows'''@abstractmethoddefcolumns(self):'''return total number of columns'''@abstractmethoddefdataslice(self,x0,y0,x1,y1):'''get slice of data from (x0, y0) to (x1, y1) inclusive'''AnyDataModelobject can be provided as the argument toRegularTableWidget. Note thatregular-tablemay make probing calls of the form (0, 0, 0, 0) to assess data limits.DevelopmentSeeCONTRIBUTING.mdfor guidelines.LicenseThis software is licensed under the Apache 2.0 license. See theLICENSEandAUTHORSfiles for details.
Package: ipyrhinoviewer
ipyrhinoviewerA Custom Jupyter Rhino 3dm ViewerUsagefromipyrhinoviewerimportRhinoViewerv=RhinoViewer(path='../examples/rhino.3dm',width=1000,height=700,ambient_light={"color":"rgb(255,255,255)","intensity":1},background_color="rgb(200,200,200)",camera_pos=[15,15,15],look_at=[0,0,0],show_axes=True,grid={"size":10,"divisions":10})Required parameterspath: path to 3dm fileOptional parameterswidth: width of the viewer in px, should be between 100 and 3000height: height of the viewer in px, should be between 100 and 3000ambient_light: adds ambient light to the scenebackground_color: changes background colorcamera_pos: sets the camera positionlook_at: sets the point where the camera orbits aroundshow_axes: adds an axes helper to the scenegrid: adds a grid helper to the sceneInstallationYou can install usingpip:pipinstallipyrhinoviewerIf you are using Jupyter Notebook 5.2 or earlier, you may also need to enable
the nbextension:jupyternbextensionenable--py[--sys-prefix|--user|--system]ipyrhinoviewerDevelopment InstallationCreate a dev environment:condacreate-nipyrhinoviewer-dev-cconda-forgenodejsyarnpythonjupyterlab
condaactivateipyrhinoviewer-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 ipyrhinoviewer
jupyter nbextension enable --sys-prefix --py ipyrhinoviewerNote 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.
Package: ipyrun
ipyrunipyrun is UI first package built on ipywidgets designed for rapidly creating an app user interface for use in a
jupyter notebook / lab or voila app. To run a script or process and generate resultant output files.
The app caches input, output and runtime data, such that the previous state of the app is available to the user when returning.Try it online!launch<-- TODO: make this work! (need to build ipyautoui)ScreenshotsThe example below shows the simple example of using theipyrunto create an App for plotting straight line
graphs, storing the input and output data for multiple runs. This is a useless example, butipyruncan be
programmed to complete any process creating user-defined input files and output files.DescriptionA generic user interface for running processes.
Data inputs to the process are defined by standard datafiles (e.g. csv, json), and data files are generated as outputs when the script is run.
A timestamped record of script execution is maintained.
An ipywdiget user interface allows users to edit script input data and view outputs of script execution.User Journey to Creating an App with the DefaultAppConfigThe rationale behind ipyrun is that by following the simple process outlined below, the user does not need to think
too much about creating an interface or how to cache data, with this being templated by the ipyrun configuration.ipyrun is "UI first",  upon which different, user-applied configurations can be attached. ipyrun ships with aDefaultRunConfigthat is intended for executing python scripts or packages. New users are recommended to use
this configuration, creating new configurations is an "advanced feature" that would require digging around the
source code to ensure understand the relationships between theconfig,actionsandui.it is possible to set up new ipyrun configurations, that might have different execute commands. 
for example it would be great to create a `RunSnake` app for executing SnakeMake commands...1create an input schema2generate an input form using ipyautoui3create a script or process to execute4create a RunApp instance to manage UI and data-cachingInstallpipinstallipyrunDevelopmambaenvcreate-fenvironment-dev.ymlpytestPackaging and Publish#  NOTE: restricted to core-maintainers onlymambaactivatehatcher# or conda env with hatch installedhatchbuild# builds to local folderhatchpublish-u__token__-a<your-pypi-token># publishes to pypi
Package: ipysankeywidget
IPython Sankey diagram widgetDisplay Sankey diagrams in IPython / Jupyter notebook usingd3-sankey-diagram.InstallationTo install use pip:$ pip install ipysankeywidgetAlternatively, to install using conda:$ conda install -c conda-forge ipysankeywidgetFor a development install, seeDEVELOPING.md.Browser supportTested in Firefox and Chrome.There have been reportsthat it's not working in Safari.DocumentationSee thed3-sankey-diagram API docs:
the attributes of theSankeyWidgetare the same as the properties of thesankeyobject described there.To change the figure size, use the ipywidgets Layout object:layout=Layout(width="1000",height="1200")SankeyWidget(links=data,layout=layout)ExamplesSee notebooks in examples folder for usage examples. You can try these online
usingBinder,
or follow the links to static versions on nbviewer:Simple exampleAdvanced examplesLinking and LayoutExporting ImagesChangelogv0.5.0 (16 December 2022)Compatible with Jupyter Lab as well as Jupyter notebook.v0.4.2 (29 April 2022)Expose d3-sankey-diagram nodePositions() option to allow for custom node layout.v0.4.1 (18 February 2021)Experimental support for link "markers"Experimental support for showing HTML info for linksv0.4.0 (19 November 2020)Update to d3-sankey-diagram v0.8.0v0.3.0Now supports Jupyterlabv0.2.5You can now show link values as SVG text elements. SetlinkLabelFormatto ad3-format string; links whose value is more thanlinkLabelMinWidthwill have a label using that format.Upgrade to d3-sankey-diagram version 0.7.3ipysankeywidget can now be installed using conda (thanks to Ali Alsabbah #33)Add a check for duplicate links (thanks to Remi Bois #23)v0.2.4Add events for clicking on nodes and links:on_node_clickedandon_link_clicked. These replace theon_selectedevent.ContributorsRick LuptonNicholas BollwegAli AlsabbahRemi BoisMiguel Mendez