Why …¶
Why nipy?¶
We are writing NIPY because we hope that it will solve several problems in the field at the moment.
We are concentrating on FMRI analysis, so we’ll put the case for that part of neuroimaging for now.
There are several good FMRI analysis packages already - for example SPM, FSL and AFNI. For each of these you can download the source code.
Like SPM, AFNI and FSL, we think source code is essential for understanding and development.
With these packages you can do many analyses. Some problems are that:
The packages don’t mix easily. You’ll have to write your own scripts to mix between them; this is time-consuming and error-prone, because you will need good understanding of each package
Because they don’t mix, researchers usually don’t try and search out the best algorithm for their task - instead they rely on the software that they are used to
Each package has its own user community, so it’s a little more difficult to share software and ideas
The core development of each language belongs in a single lab.
Another, more general problem, is planning for the future. We need a platform that can be the basis for large scale shared development. For various reasons, it isn’t obvious to us that any of these three is a good choice for common, shared development. In particular, we think that Python is the obvious choice for a large open-source software project. By comparison, matlab is not sufficiently general or well-designed as a programming language, and C / C++ are too hard and slow for scientific programmers to read or write. See why-python for this argument in more detail.
We started NIPY because we want to be able to:
support an open collaborative development environment. To do this, we will have to make our code very easy to understand, modify and extend. If make our code available, but we are the only people who write or extend it, in practice, that is closed software.
make the tools that allow developers to pick up basic building blocks for common tasks such as registration and statistics, and build new tools on top.
write a scripting interface that allows you to mix in routines from the other packages that you like or that you think are better than the ones we have.
design ways of interacting with the data and analysis stream that help you organize both. That way you can more easily keep track of your analyses. We also hope this will make analyses easier to run in parallel, and therefore much faster.
Why python?¶
The choice of programming language has many scientific and practical consequences. Matlab is an example of a high-level language. Languages are considered high level if they are able to express a large amount of functionality per line of code; other examples of high level languages are Python, Perl, Octave, R and IDL. In contrast, C is a low-level language. Low level languages can achieve higher execution speed, but at the cost of code that is considerably more difficult to read. C++ and Java occupy the middle ground sharing the advantages and the disadvantages of both levels.
Low level languages are a particularly ill-suited for exploratory scientific computing, because they present a high barrier to access by scientists that are not specialist programmers. Low-level code is difficult to read and write, which slows development ([Prechelt2000ECS], [boehm1981], [Walston1977MPM]) and makes it more difficult to understand the implementation of analysis algorithms. Ultimately this makes it less likely that scientists will use these languages for development, as their time for learning a new language or code base is at a premium. Low level languages do not usually offer an interactive command line, making data exploration much more rigid. Finally, applications written in low level languages tend to have more bugs, as bugs per line of code is approximately constant across many languages [brooks78].
In contrast, interpreted, high-level languages tend to have easy-to-read syntax and the native ability to interact with data structures and objects with a wide range of built-in functionality. High level code is designed to be closer to the level of the ideas we are trying to implement, so the developer spends more time thinking about what the code does rather than how to write it. This is particularly important as it is researchers and scientists who will serve as the main developers of scientific analysis software. The fast development time of high-level programs makes it much easier to test new ideas with prototypes. Their interactive nature allows researchers flexible ways to explore their data.
SPM is written in Matlab, which is a high-level language specialized for matrix algebra. Matlab code can be quick to develop and is relatively easy to read. However, Matlab is not suitable as a basis for a large-scale common development environment. The language is proprietary and the source code is not available, so researchers do not have access to core algorithms making bugs in the core very difficult to find and fix. Many scientific developers prefer to write code that can be freely used on any computer and avoid proprietary languages. Matlab has structural deficiencies for large projects: it lacks scalability and is poor at managing complex data structures needed for neuroimaging research. While it has the ability to integrate with other languages (e.g., C/C++ and FORTRAN) this feature is quite impoverished. Furthermore, its memory handling is weak and it lacks pointers - a major problem for dealing with the very large data structures that are often needed in neuroimaging. Matlab is also a poor choice for many applications such as system tasks, database programming, web interaction, and parallel computing. Finally, Matlab has weak GUI tools, which are crucial to researchers for productive interactions with their data.
Boehm, Barry W. (1981) Software Engineering Economics. Englewood Cliffs, NJ: Prentice-Hall.
Prechelt, Lutz. 2000. An Empirical Comparison of Seven Programming Languages. IEEE Computer 33, 23–29.
Walston, C E, and C P Felix. 1977. A Method of Programming Measurement and Estimation. IBM Syst J 16, 54-73.