Lightweight GUI for sorting MELODIC ICA components.
Project description
PICAchooser (the package)
A set of simple gui tools for scanning through MELODIC probabalistic ICA runs and quickly making decisions about which components to retain, and what relates to what. These tools each only do one thing, but they do them quickly and easily using only keyboard input. Current programs are PICAchooser, melodicomp, and grader.
PICAchooser screenshot
Support
This code base is being developed and supported by a grant from the US NIH 1R01 NS097512.
Additional packages used
PICAchooser would not be possible without many additional open source packages. These include:
pyqtgraph:
Luke Campagnola. PyQtGraph: Scientific Graphics and GUI Library for Python
nibabel:
numpy:
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:
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:
McKinney, W., pandas: a foundational Python library for data analysis and statistics. Python for High Performance and Scientific Computing,
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