SKIP 2 — scikit-image mission statement
Author: | Juan Nunez-Iglesias <juan.nunez-iglesias@monash.edu> |
Author: | Stéfan van der Walt <stefanv@berkeley.edu> |
Author: | Josh Warner |
Author: | François Boulogne |
Author: | Emmanuelle Gouillart |
Author: | Mark Harfouche |
Author: | Lars Grüter |
Author: | Egor Panfilov |
Author: | Gregory Lee |
Status: | Active |
Type: | Process |
Created: | 2018-12-08 |
Resolved: | |
Resolution: | |
skimage-Version: |
| 0.16 |
Abstract
scikit-image should adopt the document below as its mission statement. This
statement will feature prominently in the scikit-image home page and readme,
as well as the contributor and core developer guides. Decisions about the API
and the future of the library would be referenced against this document. (See
SKIP 1 — scikit-image governance and decision-making.)
In July 2018, I (Juan) published a blog post that broadly outlined what I would
want from a roadmap for scikit-image , but requested comments from the
community before it would be finalized. I consider that we have collected
comments for long enough and can move forward with formal adoption. Most
comments were positive, so below I’ll just summarize the “negative” comments
under “rejected ideas”.
Detailed description
(Or: What problem does this proposal solve?)
Over the past few years, scikit-image has been slightly “adrift”, with new and
old contributors coming in, adding what small bits they need at the time, and
disappearing again for a while. This is fine and we want to encourage more of
it, but it also lacks direction. Additionally, without a concerted roadmap to
concentrate effort, many of these contributions just fall by the wayside, as it
is difficult for new contributors to live up to our stringent (and largely
unwritten) standards of code.
Implementation
Our mission
scikit-image aims to be the reference library for scientific image analysis in
Python. We accomplish this by:
- being easy to use and install. We are careful in taking on new
dependencies, and sometimes cull existing ones, or make them optional. All
functions in our API have thorough docstrings clarifying expected inputs and
outputs.
- providing a consistent API. Conceptually identical arguments have the
same name and position in a function signature.
- ensuring correctness. Test coverage is close to 100% and code is reviewed by
at least two core developers before being included in the library.
- caring for users’ data. We have a functional API and don’t modify
input arrays unless explicitly directed to do so.
- promoting education in image processing, with extensive pedagogical
documentation.
Our values
- We are inclusive. We continue to welcome and mentor newcomers who are
making their first contribution.
- We are community-driven. Decisions about the API and features are driven by
our users’ requirements, not by the whims of the core team. (See
SKIP 1 — scikit-image governance and decision-making.)
- We serve scientific applications primarily, over “consumer” image editing in
the vein of Photoshop or GIMP. This often means prioritizing n-dimensional
data support, and rejecting implementations of “flashy” filters that have
little scientific value.
- We value simple, readable implementations over getting every last ounce of
performance. Readable code that is easy to understand, for newcomers and
maintainers alike, makes it easier to contribute new code as well as prevent
bugs. This means that we will prefer a 20% slowdown if it reduces lines of
code two-fold, for example.
- We value education and documentation. All functions should have NumPy-style
docstrings , preferably with examples, as well as gallery
examples that showcase how that function is used in a scientific application.
Core developers take an active role in finishing documentation examples.
- We don’t do magic. We use NumPy arrays instead of fancy façade objects
, and we prefer to educate users rather than make decisions on their
behalf. This does not preclude sensible defaults.
This document
Much in the same way that the Zen of Python and PEP8 guide style and
implementation details in most Python code, this guide is meant to guide
decisions about the future of scikit-image, be it in terms of code style,
whether to accept new functionality, or whether to take on new dependencies,
among other things.
References
To find out more about the history of this document, please read the following:
- Original blog post
- The GitHub issue
- The image.sc forum post
- The SKIP GitHub pull request
Footnotes
Backward compatibility
This SKIP formalizes what had been the unwritten culture of scikit-image, so it
does not raise any backward compatibility concerns.
Alternatives
Two topics in the original discussion were ultimately rejected, detailed below:
Magical thinking
Philipp Hanslovsky suggested that, regarding “doing magic”, it is
advisable in some contexts, and a good solution is to provide a magic layer
built on top of the non-magical one. I agree with this assessment, but, until
1.0, scikit-image should remain the non-magic layer.
Discussion
See References below.
References
Copyright
This document is dedicated to the public domain with the Creative Commons CC0
license . Attribution to this source is encouraged where appropriate, as per
CC0+BY .