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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 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 sofware.
  • 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.

You can read more about the project history here: History.