https://github.com/brisvag/napari

napari: a fast, interactive, multi-dimensional image viewer for python

Science Score: 41.0%

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Low similarity (14.9%) to scientific vocabulary

Last synced: 10 months ago · JSON representation ·

Repository

napari: a fast, interactive, multi-dimensional image viewer for python

Basic Info

Host: GitHub
Owner: brisvag
License: bsd-3-clause
Language: Python
Default Branch: main
Homepage: https://napari.org
Size: 39.4 MB

Statistics

Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Releases: 0

Fork of napari/napari

Created over 5 years ago · Last pushed 10 months ago

Metadata Files

Readme Funding License Citation Codeowners

napari

multi-dimensional image viewer for python

napari is a fast, interactive, multi-dimensional image viewer for Python. It's designed for browsing, annotating, and analyzing large multi-dimensional images. It's built on top of Qt (for the GUI), vispy (for performant GPU-based rendering), and the scientific Python stack (numpy, scipy).

We're developing napari in the open! But the project is in an alpha stage, and there will still likely be breaking changes with each release. You can follow progress on this repository, test out new versions as we release them, and contribute ideas and code.

If you want to refer to our documentation, please go to napari.org. If you want to contribute to it, please refer to the contributing section below.

We're working on tutorials, but you can also quickly get started by looking below.

installation

It is recommended to install napari into a virtual environment, like this:

sh conda create -y -n napari-env -c conda-forge python=3.9 conda activate napari-env python -m pip install "napari[all]"

If you prefer conda over pip, you can replace the last line with: conda install -c conda-forge napari pyqt

See here for the full installation guide.

simple example

(The examples below require the scikit-image package to run. We just use data samples from this package for demonstration purposes. If you change the examples to use your own dataset, you may not need to install this package.)

From inside an IPython shell, you can open up an interactive viewer by calling

```python from skimage import data import napari

viewer = napari.viewimage(data.cells3d(), channelaxis=1, ndisplay=3) ```

napari viewer with a multichannel image of cells displayed as two image layers: nuclei and membrane.

To use napari from inside a script, use napari.run():

```python from skimage import data import napari

viewer = napari.viewimage(data.cells3d(), channelaxis=1, ndisplay=3) napari.run() # start the "event loop" and show the viewer ```

features

Check out the scripts in our examples folder to see some of the functionality we're developing!

napari supports six main different layer types, Image, Labels, Points, Vectors, Shapes, and Surface, each corresponding to a different data type, visualization, and interactivity. You can add multiple layers of different types into the viewer and then start working with them, adjusting their properties.

All our layer types support n-dimensional data and the viewer provides the ability to quickly browse and visualize either 2D or 3D slices of the data.

napari also supports bidirectional communication between the viewer and the Python kernel, which is especially useful when launching from jupyter notebooks or when using our built-in console. Using the console allows you to interactively load and save data from the viewer and control all the features of the viewer programmatically.

You can extend napari using custom shortcuts, key bindings, and mouse functions.

tutorials

For more details on how to use napari checkout our tutorials. These are still a work in progress, but we'll be updating them regularly.

mission, values, and roadmap

For more information about our plans for napari you can read our mission and values statement, which includes more details on our vision for supporting a plugin ecosystem around napari. You can see details of the project roadmap here.

contributing

Contributions are encouraged! Please read our contributing guide to get started. Given that we're in an early stage, you may want to reach out on our Github Issues before jumping in.

If you want to contribute or edit to our documentation, please go to napari/docs.

code of conduct

napari has a Code of Conduct that should be honored by everyone who participates in the napari community.

governance

You can learn more about how the napari project is organized and managed from our governance model, which includes information about, and ways to contact the @napari/steering-council and @napari/core-devs.

citing napari

If you find napari useful please cite this repository using its DOI as follows:

napari contributors (2019). napari: a multi-dimensional image viewer for python. doi:10.5281/zenodo.3555620

Note this DOI will resolve to all versions of napari. To cite a specific version please find the DOI of that version on our zenodo page. The DOI of the latest version is in the badge at the top of this page.

help

We're a community partner on the image.sc forum and all help and support requests should be posted on the forum with the tag napari. We look forward to interacting with you there.

Bug reports should be made on our github issues using the bug report template. If you think something isn't working, don't hesitate to reach out - it is probably us and not you!

institutional and funding partners

Owner

Name: Lorenzo Gaifas
Login: brisvag
Kind: user
Company: @gutsche-lab

Twitter: brisvag
Repositories: 16
Profile: https://github.com/brisvag

PhD student at @gutsche-lab, doing computational stuff with cryo-ET data.

Citation (CITATION.cff)

cff-version: 1.2.0
message: If you use this software, please cite it using these metadata.
title: 'napari: a multi-dimensional image viewer for Python'
doi: 10.5281/zenodo.3555620
authors:
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  family-names: Ahlers
  affiliation: Monash University
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  affiliation: Quansight
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  affiliation: Harvard Medical School, BIDMC
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  affiliation: University of Warsaw, Faculty of Mathematics, Informatics, and Mechanics
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  affiliation: Chan Zuckerberg Biohub
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  family-names: Can Solak
  affiliation: Chan Zuckerberg Biohub
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  affiliation: Laboratory for Optics and Biosciences, Ecole Polytechnique, INSERM,
    CNRS, Palaiseau, France
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  family-names: Freeman
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- given-names: Lorenzo
  family-names: Gaifas
  affiliation: Gutsche Lab - University of Grenoble
  orcid: https://orcid.org/0000-0003-4875-9422
  alias: brisvag
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  family-names: Gohlke
  affiliation: University of California, Irvine
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repository-code: https://github.com/napari/napari
license: BSD-3-Clause

GitHub Events

Total

Delete event: 1
Issue comment event: 1
Push event: 319
Pull request review event: 2
Pull request event: 10
Create event: 56

Last Year

Delete event: 1
Issue comment event: 1
Push event: 319
Pull request review event: 2
Pull request event: 10
Create event: 56

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ecosyste.ms

Data

Tools

Indexes

Applications

Experiments

Open Source Science

https://github.com/brisvag/napari

Science Score: 41.0%

Repository

Basic Info

Statistics

Metadata Files

README.md

napari

multi-dimensional image viewer for python

installation

simple example

features

tutorials

mission, values, and roadmap

contributing

code of conduct

governance

citing napari

help

institutional and funding partners

Owner

Citation (CITATION.cff)

GitHub Events

Total

Last Year

Dependencies