|Mentor||Juan Nunez-Iglesias (jni)|
|Suitable for Beginners?||no|
|Tags||python image visualization n-dimensional array qt GUI OpenGL|
|Applications (1st Choice)||7 (7 submitted | 0 in-progress)|
|Applications (2nd Choice)||5 (5 submitted | 0 in-progress)|
|Code of Conduct||https://github.com/napari/napari/blob/master/docs/developers/CODE_OF_CONDUCT.md|
|License||BSD 3-Clause "New" or "Revised" License (BSD-3-Clause)|
napari is an n-dimensional array viewer for Python. We aim to make it easy and fast to visualize n-dimensional arrays (sometimes called tensors) and related data (such as point coordinates, surfaces, and vector fields). napari is based on NumPy, Vispy, Dask, QtPy, and more. In addition to visualization, napari aims to provide an entry point to scientific Python software for non-programmers.
In addition to myself, various other members of the core development team will help mentor students, including Nick Sofroniew (@sofroniewn), Talley Lambert (@tlambert03), and Kira Evans (@kne42).
Some experience working with Python and NumPy or with Matlab (which translates quite easily to Python+NumPy) is essential. Experience developing GUIs or working with OpenGL could be useful but is absolutely not required.
Tasks And Features
We want napari to become an extensible platform that others build upon. For that purpose, we are building plugin machinery, but for now, guidelines on how to build a high-quality napari plugin are lacking. The closest thing we have is this GitHub answer by one of our developers, Talley Lambert.
We are happy to work with students on any of our open issues, but we specifically think that creating some demonstration plugins, or software that builds on top of napari, would be a fantastic way to contribute to the project. At this early stage, developing plugins is useful not just for the creation plugin, but as a conversation between the plugin and the project, which can update its plugin infrastructure and documentation in response to plugin developers' needs.
We created an issue tracking these projects here (#939). A small sample:
- interactive segmentation (dividing images into meaningful regions)
- interactive image alignment by picking correspondence points
- a viewer for deep learning data and models in the style of tensorboard
- integrate napari's image viewer with matplotlib plots
- 3D tracking viewer
We also have issues tagged with "good first issue" for newcomers to get introduced to the code.
If you are thinking of applying to this project for RGSoC 2020 and have any questions, feel free to contact the project mentor by leaving a comment below or using the following channels:
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Sara Latif, Monday, March 30, 11:12 UTC
Hi, My Team is super excited about this project, we bear all skills except Open GL, soon we are gonna start Open GL too, and excited to participate in this project.
Pakhi2001, Sunday, March 29, 15:05 UTC
I would like to contribute in this project. Please help me started.
Natasha Murashkina, Friday, March 27, 12:55 UTC
Hi! My teammate Maria and I have applied to napari! We've successfully installed the library and checked out some examples. We can't contribute right now due to midterms but we hope to have a productive summer with you!
Juan Nunez-Iglesias, Wednesday, March 25, 00:48 UTC
And also note all of the warnings from the RGSoC team: it is not a requirement to contribute to a project to get accepted. =)
Juan Nunez-Iglesias, Wednesday, March 25, 00:47 UTC
Hi Ira, Sheeya, and welcome! =) Please follow the discussion on our zulip chat channel! https://napari.zulipchat.com/#narrow/stream/212875-general/topic/RGSoC
The basic idea is that you should find some cool dataset that you are interested in exploring/playing with, try to get it into napari, and then write a tutorial on how you did it, either in your own blog or to submit to https://github.com/napari/tutorials, where if accepted it will appear at https://napari.org/tutorials!
Ira Aggarwal, Monday, March 23, 10:17 UTC
Hello! We are Ira and Shreeya from Team C018 . We are interested in contributing towards the project for RGSoC - we have installed napari and gone through the tutorials on the website. We're thinking of starting issues labelled 'good first issues' but Is there any specific task we can look into instead?
tea-n-biccies RGSoC, Friday, March 20, 13:43 UTC
Dear RGSoC applicants - we have added a new FAQ page to the website. Please check this out before asking mentors your questions, as we may already have an answer for you :)
Further details of how to apply to RGSoC (by 23:00 UTC on 30 March 2020) can be found at https://railsgirlssummerofcode.org/students
Dina Adel Shalabi, Tuesday, March 17, 22:08 UTC
Hi Juan!, I'm Dina and my teammate Dalia from team Bro-grammers ! We're very interested to contribute to your project, we've installed napari and tried it, and we were astonished by the great work you've done, so could you please guide us on what we should do next?
tea-n-biccies RGSoC, Monday, March 9, 11:23 UTC
Hi everyone - the RGSoC team here :)
Just a reminder that student applications are open until 23:00 UTC on 30 March 2020.
For information on how to apply as a student so you can work on this project with RGSoC, please read the guidance at https://railsgirlssummerofcode.org/students
Juan Nunez-Iglesias, Friday, March 6, 06:10 UTC
Hi Gayathri! (And Niharika!) Thanks for your interest in the project! I would say that the easiest way would be to read the announcement blog post (https://ilovesymposia.com/2019/10/24/introducing-napari-a-fast-n-dimensional-image-viewer-in-python/) and the tutorials at https://napari.org, then try to find cool datasets online to add to napari. For example, writing code to read data from the imaging data resource (https://idr.openmicroscopy.org) into napari would be an interesting way to get started with the project!
vellanki gayathri, Thursday, March 5, 13:30 UTC
I am Gayathri Vellanki and my teammate is Niharika M.We are from team Linux Lions and looking forward to participate in RGSoC.We really Interested In your project. Though all the details mentioned give clear understanding of the project but can you guide us about - at initial phase what tasks we are supposed to do. And how and where we can get started?