A Tour of Trellis Graphics

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ABSTRACT This document leads you through Trellis graphics: it shows the functions in the Trellis library, it describes the common arguments that the functions share, and shows how Trellis displays are customized for various graphical devices. Other information is available about Trellis, including a user's manual and a journal article with data analysis examples. To find these and more, refer to the Trellis web page:

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    ABSTRACT: Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statistics, and numerical methods. As part of the MNE software suite, MNE-Python is an open-source software package that addresses this challenge by providing state-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysis pipelines by writing Python scripts. Moreover, MNE-Python is tightly integrated with the core Python libraries for scientific comptutation (NumPy, SciPy) and visualization (matplotlib and Mayavi), as well as the greater neuroimaging ecosystem in Python via the Nibabel package. The code is provided under the new BSD license allowing code reuse, even in commercial products. Although MNE-Python has only been under heavy development for a couple of years, it has rapidly evolved with expanded analysis capabilities and pedagogical tutorials because multiple labs have collaborated during code development to help share best practices. MNE-Python also gives easy access to preprocessed datasets, helping users to get started quickly and facilitating reproducibility of methods by other researchers. Full documentation, including dozens of examples, is available at
    Frontiers in Neuroscience 12/2013; 7:267. DOI:10.3389/fnins.2013.00267
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    ABSTRACT: The most common techniques for graphically presenting a multivariate dataset involve projection onto a one or two-dimensional subspace. Interpretation of such plots is not always straightforward because projections are smoothing operations in that structure can be obscured by projection but never enhanced. In this paper an alternative procedure for finding interesting features is proposed that is based on locating the modes of an induced hyperspherical density function, and a simple algorithm for this purpose is developed. Emphasis is placed on identifying the non-linear effects, such as clustering, so to this end the data are firstly sphered to remove all of the location, scale and correlational structure. A set of simulated bivariate data and artistic qualities of painters data are used as examples.
    Statistics and Computing 11/1998; 8(4):347-355. DOI:10.1023/A:1008828723097 · 1.75 Impact Factor


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