picachooser 1.4.3

PICAchooser

Lets you step through the components in an ICA analysis (from many sources), and select which components you want to retain. In addition to showing the spatial ICs, it also displays the componnent timecourses, motion traces, and the correlation between them, to help with your decision making.

Once you launch, you do everything with keyboard commands, and it’s been optimized to go as fast as possible, so you aren’t waiting around for things.

melodicomp

Puts up two melodic IC files side by side. In order to make the comparison meaningful, it first calculates the spatial crosscorrelation between each IC in the first file and each IC in the second. As you step through components in the first file, on the right you see the component with the highest crosscorrelation in the second file. You can sort either by IC order in the first file (i.e. in order of descending variance explained), or in descending correlation coefficient (i.e. best matched components first). When you quit (or hit the escape key), it writes out a file listing the best matched ICs along with their correlation coefficients.

I’m especially proud of the “blink” feature. When you hit the “b” key, the right and left window swap, instantaneously. This lets you see what changes between the two sets of networks in a very natural way. This is inspired by blink comparators, a cool old piece of tech probably long forgotten by most.

Again, once you launch, you do everything with keyboard commands, and it’s been optimized to go as fast as possible, so you aren’t waiting around for things.

pyqtgraph:

1. Luke Campagnola. PyQtGraph: Scientific Graphics and GUI Library for Python

nibabel:

1. Nibabel: Python package to access a cacophony of neuro-imaging file formats | https://10.5281/zenodo.591597

numpy:

1. Stéfan van der Walt, S. Chris Colbert and Gaël Varoquaux. The NumPy Array: A Structure for Efficient Numerical Computation, Computing in Science & Engineering, 13, 22-30 (2011) | https:10.1109/MCSE.2011.37

scipy:

1. Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, CJ Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E.A. Quintero, Charles R Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors. (2020) SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods, 17, 261–272 (2020) | https://doi.org/10.1038/s41592-019-0686-2

pandas:

1. McKinney, W., pandas: a foundational Python library for data analysis and statistics. Python for High Performance and Scientific Computing, 2011. 14.