Niwidgets: Neuroimaging Widgets for Jupyter Notebooks

Neuroimaging Widgets (niwidgets)

This repository is supposed to provide easy and general wrappers to display interactive widgets that visualise standard-format neuroimaging data, using new functions and standard functions from other libraries. It looks like this:

Install via:

pip install niwidgets

Or, to get the most up-to-date development version from github:

pip install git+git://github.com/nipy/niwidgets/

It requires nibabel and nilearn:

pip install nibabel nilearn

Check out the examples using the code in this notebook here: https://github.com/nipy/niwidgets/blob/master/index.ipynb (you need to run the notebook on your local machine to use the interactive features).

or using binder here: https://mybinder.org/v2/gh/nipy/niwidgets/master?filepath=index.ipynb

Usage:

There are currently three supported widgets:

Volume widgets. This widget is primarily designed to mimic existing tools such as , but it also allows you to wrap plots from the nilearn plotting library to make them interactive.

Surface widgets. This widget takes freesurfer-generated volume files and turns them into widgets using the ipyvolume library. It allows you to add different overlays for the surface files.

Streamline widgets. This widget accepts .trk files and displays the tracts using ipyvolume.

To see how to use these widgets, please check the documentation.

As an example of how you might generate a Volume widget:

from niwidgets import NiftiWidget

my_widget = NiftiWidget('./path/to/file.nii')

You can then create a plot either with the default nifti plotter:

my_widget.nifti_plotter()

This will give you sliders to slice through the image, and an option to set the colormap.

You can also provide your own plotting function:

import nilearn.plotting as nip

my_widget.nifti_plotter(plotting_func=nip.plot_glass_brain)

By default, this will give you the following interactive features: - selecting a colormap - if supported by the plotting function, x-y-x sliders (e.g. for nip.plot_img)

You can, however, always provide features you would like to have interactive yourself. This follows the normal ipywidgets format. For example, if you provide a list of strings for a keyword argument, this becomes a drop-down menu. If you provide a tuple of two numbers, this becomes a slider. Take a look at some examples we have in this notebook (you need to run the notebook on your local machine to use the interactive features).

Hopefully we will be able to add more default interactive features in the future, as well as plotting of other data (such as surface projections). If you have any suggestions for plot features to be added, please let us know - or add them yourself and create a pull request!

Development

Contributing

Please contribute! When writing new widgets, please make sure you include example data that allows users to try a widget without having to munge their data into the right format first.

Please also make sure you write a test for your new widget. It's hard to test jupyter widgets, but it would be great if you could at least write a test that "instantiates" a widget. This allows us to maintain a stable release.

Development installation

As always with pip packages, you can install a "development" version of this package by cloning the git repository and installing it via pip install -e /path/to/package.

Updating the documentation

To update the documentation, you can do the following things:

Make your changes on a separate branch, such as DOC/update-api-documentation.
Merge your branch into master Make sure you have the packages in
doc-requirements.txt installed Run make gh-pages in the root directory of
the repository

This should run sphinx to generate the documentation, push it to the gh-pages branch, and then revert to master.

Author: nipy
Source Code: https://github.com/nipy/niwidgets
License: View license

#python #jupyter

What is GEEK

Buddha Community

Niwidgets: Neuroimaging Widgets for Jupyter Notebooks

Catharine Rippin

1591267200

Rodrigo Senra - Jupyter Notebooks

Nosso convidado de hoje é diretor técnico na Work & Co, PhD em Ciências da Computação, já contribuiu com inúmeros projetos open source em Python, ajudou a fundar a Associação Python Brasil e já foi premiado com o Prêmio Dorneles Tremea por contribuições para a comunidade Python Brasil.

#alexandre oliva #anaconda #apache zeppelin #associação python brasil #azure notebooks #beakerx #binder #c++ #closure #colaboratory #donald knuth #fernando pérez #fortran #graphql #guido van rossum #ipython #java #javascript #json #jupyter kenels #jupyter notebooks #jupyterhub #jupyterlab #latex #lisp #literate programming #lua #matlab #perl #cinerdia #prêmio dorneles tremea #python #r #rodrigo senra #scala #spark notebook #tcl #typescript #zope

Chloe Butler

1650276000

Niwidgets: Neuroimaging Widgets for Jupyter Notebooks

Neuroimaging Widgets (niwidgets)

This repository is supposed to provide easy and general wrappers to display interactive widgets that visualise standard-format neuroimaging data, using new functions and standard functions from other libraries. It looks like this:

Install via:

pip install niwidgets

Or, to get the most up-to-date development version from github:

pip install git+git://github.com/nipy/niwidgets/

It requires nibabel and nilearn:

pip install nibabel nilearn

Check out the examples using the code in this notebook here: https://github.com/nipy/niwidgets/blob/master/index.ipynb (you need to run the notebook on your local machine to use the interactive features).

or using binder here: https://mybinder.org/v2/gh/nipy/niwidgets/master?filepath=index.ipynb

Usage:

There are currently three supported widgets:

Volume widgets. This widget is primarily designed to mimic existing tools such as , but it also allows you to wrap plots from the nilearn plotting library to make them interactive.

Surface widgets. This widget takes freesurfer-generated volume files and turns them into widgets using the ipyvolume library. It allows you to add different overlays for the surface files.

Streamline widgets. This widget accepts .trk files and displays the tracts using ipyvolume.

To see how to use these widgets, please check the documentation.

As an example of how you might generate a Volume widget:

from niwidgets import NiftiWidget

my_widget = NiftiWidget('./path/to/file.nii')

You can then create a plot either with the default nifti plotter:

my_widget.nifti_plotter()

This will give you sliders to slice through the image, and an option to set the colormap.

You can also provide your own plotting function:

import nilearn.plotting as nip

my_widget.nifti_plotter(plotting_func=nip.plot_glass_brain)

By default, this will give you the following interactive features: - selecting a colormap - if supported by the plotting function, x-y-x sliders (e.g. for nip.plot_img)

You can, however, always provide features you would like to have interactive yourself. This follows the normal ipywidgets format. For example, if you provide a list of strings for a keyword argument, this becomes a drop-down menu. If you provide a tuple of two numbers, this becomes a slider. Take a look at some examples we have in this notebook (you need to run the notebook on your local machine to use the interactive features).

Hopefully we will be able to add more default interactive features in the future, as well as plotting of other data (such as surface projections). If you have any suggestions for plot features to be added, please let us know - or add them yourself and create a pull request!

Development

Contributing

Please contribute! When writing new widgets, please make sure you include example data that allows users to try a widget without having to munge their data into the right format first.

Please also make sure you write a test for your new widget. It's hard to test jupyter widgets, but it would be great if you could at least write a test that "instantiates" a widget. This allows us to maintain a stable release.

Development installation

As always with pip packages, you can install a "development" version of this package by cloning the git repository and installing it via pip install -e /path/to/package.

Updating the documentation

To update the documentation, you can do the following things:

Make your changes on a separate branch, such as DOC/update-api-documentation.
Merge your branch into master Make sure you have the packages in
doc-requirements.txt installed Run make gh-pages in the root directory of
the repository

This should run sphinx to generate the documentation, push it to the gh-pages branch, and then revert to master.

Author: nipy
Source Code: https://github.com/nipy/niwidgets
License: View license

#python #jupyter

Chesley Labadie

1672193648

Notebook: Jupyter interactive Notebook

Jupyter Notebook

The Jupyter notebook is a web-based notebook environment for interactive computing.

Jupyter notebook example

Maintained versions

We maintain the two most recently released major versions of Jupyter Notebook, Notebook v5 and Classic Notebook v6. After Notebook v7.0 is released, we will no longer maintain Notebook v5. All Notebook v5 users are strongly advised to upgrade to Classic Notebook v6 as soon as possible.

The Jupyter Notebook project is currently undertaking a transition to a more modern code base built from the ground-up using JupyterLab components and extensions.

There is new stream of work which was submitted and then accepted as a Jupyter Enhancement Proposal (JEP) as part of the next version (v7): https://jupyter.org/enhancement-proposals/79-notebook-v7/notebook-v7.html

There is also a plan to continue maintaining Notebook v6 with bug and security fixes only, to ease the transition to Notebook v7: https://github.com/jupyter/notebook-team-compass/issues/5#issuecomment-1085254000

Notebook v7

The next major version of Notebook will be based on:

JupyterLab components for the frontend
Jupyter Server for the Python server

This represents a significant change to the jupyter/notebook code base.

To learn more about Notebook v7: https://jupyter.org/enhancement-proposals/79-notebook-v7/notebook-v7.html

Classic Notebook v6

Maintainance and security-related issues are now being addressed in the 6.4.x branch.

A 6.5.x branch will be soon created and will depend on nbclassic for the HTML/JavaScript/CSS assets.

New features and continuous improvement is now focused on Notebook v7 (see section above).

If you have an open pull request with a new feature or if you were planning to open one, we encourage switching over to the Jupyter Server and JupyterLab architecture, and distribute it as a server extension and / or JupyterLab prebuilt extension. That way your new feature will also be compatible with the new Notebook v7.

Jupyter notebook, the language-agnostic evolution of IPython notebook

Jupyter notebook is a language-agnostic HTML notebook application for Project Jupyter. In 2015, Jupyter notebook was released as a part of The Big Split™ of the IPython codebase. IPython 3 was the last major monolithic release containing both language-agnostic code, such as the IPython notebook, and language specific code, such as the IPython kernel for Python. As computing spans across many languages, Project Jupyter will continue to develop the language-agnostic Jupyter notebook in this repo and with the help of the community develop language specific kernels which are found in their own discrete repos.

Installation

You can find the installation documentation for the Jupyter platform, on ReadTheDocs. The documentation for advanced usage of Jupyter notebook can be found here.

For a local installation, make sure you have pip installed and run:

pip install notebook

Usage - Running Jupyter notebook

Running in a local installation

Launch with:

jupyter notebook

Running in a remote installation

You need some configuration before starting Jupyter notebook remotely. See Running a notebook server.

Development Installation

See CONTRIBUTING.md for how to set up a local development installation.

Contributing

If you are interested in contributing to the project, see CONTRIBUTING.md.

Community Guidelines and Code of Conduct

This repository is a Jupyter project and follows the Jupyter Community Guides and Code of Conduct.

Resources

Download Details:

Author: Jupyter
Source Code: https://github.com/jupyter/notebook
License: View license

#jupyter #notebook

Chesley Labadie

1672238100

Ipywidgets: Interactive Widgets for The Jupyter Notebook

ipywidgets: Interactive HTML Widgets

ipywidgets, also known as jupyter-widgets or simply widgets, are interactive HTML widgets for Jupyter notebooks and the IPython kernel.

Notebooks come alive when interactive widgets are used. Users gain control of their data and can visualize changes in the data.

Learning becomes an immersive, fun experience. Researchers can easily see how changing inputs to a model impact the results. We hope you will add ipywidgets to your notebooks, and we're here to help you get started.

Core Interactive Widgets

The fundamental widgets provided by this library are called core interactive widgets. A demonstration notebook provides an overview of the core interactive widgets, including:

sliders
progress bars
text boxes
toggle buttons and checkboxes
display areas
and more

Jupyter Interactive Widgets as a Framework

Besides the widgets already provided with the library, the framework can be extended with the development of custom widget libraries. For detailed information, please refer to the ipywidgets documentation.

Cookiecutter template for custom widget development

A template project for building custom widgets is available as a cookiecutter. This cookiecutter project helps custom widget authors get started with the packaging and the distribution of their custom Jupyter interactive widgets. The cookiecutter produces a project for a Jupyter interactive widget library following the current best practices for using interactive widgets. An implementation for a placeholder "Hello World" widget is provided as an example.

Popular widget libraries such as bqplot, pythreejs and ipyleaflet follow exactly the same template and directory structure. They serve as more advanced examples of usage of the Jupyter widget infrastructure.

Popular custom widget examples

Examples of custom widget libraries built upon ipywidgets are

bqplot a 2d data visualization library enabling custom user interactions.
pythreejs a Jupyter - Three.js wrapper, bringing Three.js to the notebook.
ipyleaflet a leaflet widget for Jupyter.

Install

The stable version of ipywidgets can be installed with pip or conda.

With pip:

pip install ipywidgets

With conda:

conda install -c conda-forge ipywidgets

Developer install from source

Installing from source is more complicated and requires a developer install, see the detailed developer install instructions.

If you want to install ipywidgets from source, you will need the yarn package manager version 1.2.1 or later. To install the latest master version from the root directory of the source code, run dev-install.sh. To only build the Python package enter pip install -e ..

Usage

See the examples section of the documentation. The widgets are being used in a variety of ways; some uses can be seen in these notebooks: Demo notebook of interactive widgets

Change log

Version Compatibility with Front-End Clients

Refer to change log for more detail.

ipywidgets	Classic Notebook	nbclassic
master	-	TBD
7.6.3		0.2.6
Legacy
6.x		-
5.x	4.2	-
4.1.x	4.1	-
4.0.x	4.0	-

Contributing to ipywidgets

Developer information

Project Jupyter resources

Weekly Team Meetings

Developer Meetings take place on zoom, on Tuesdays at 9:30AM Pacific Time (your time).

Minutes are taken at Hackmd.io.

Download Details:

Author: jupyter-widgets
Source Code: https://github.com/jupyter-widgets/ipywidgets
License: BSD-3-Clause license

#jupyter #widgets

Sarai Thompson

1625284380

Get started with Jupyter Notebook

Jupyter Notebook is an online computational notebook that allows you to combine code, comments, media, and visualizations in interactive documents. It has quickly become one of the most popular online computational notebooks, used by top companies such as Google, Microsoft, and NASA. Today, we’re going to explore Jupyter Notebooks and discuss their benefits and how to get started.

We’ll cover:

#jupyter #python #jupyter-notebook

Purpose	Badges
Latest (master: future 8.0)
Stable
Communication