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Unified appearance of entries a bit
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‎flier/eppy-handout.tex

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%___________________________________________________________________________
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% Your logo placed under ../pics called as <project>_logo.svg
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% .svg is preferable, otherwise some other vector (.pdf) or even
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% raster (.png) would suffice
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\ndproject{XXX}{http://example.org}{opensesame_logo.pdf}{.2}{-0.25em}{0em}
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%\begin{figure}
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%\includegraphics[width=0.3\columnwidth]{../pics/psychopy_logo.pdf}
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%\end{figure}
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Brief description.
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\begin{itemize}[nolistsep,topsep=0em,leftmargin=1pc]
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\item The most interesting
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\item and
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\item methodology oriented
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\item features
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\item ideally with limited selection of citations
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\end{itemize}
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% Your favorite screenshot placed under ../pics/
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% named as <project>_screenshot.png (optional numbers in suffixes if
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% you have multiple to choose from)
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\includegraphics[width=\columnwidth]{opensesame_screenshot1.png}
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% Selected set of citations, Here is an example:
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\ndcite{D. Geffroy, D. Rivière, I. Denghien, N. Souedet,
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S. Laguitton, and Y. Cointepas. BrainVISA: a complete software
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platform for neuroimaging. In Python in Neuroscience workshop,
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Paris, Aug. 2011.}
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% % Your logo placed under ../pics called as <project>_logo.svg
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% % .svg is preferable, otherwise some other vector (.pdf) or even
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% % raster (.png) would suffice
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% \ndproject{XXX}{http://example.org}{opensesame_logo.pdf}{.2}{-0.25em}{0em}
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%
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% %\begin{figure}
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% %\includegraphics[width=0.3\columnwidth]{../pics/psychopy_logo.pdf}
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% %\end{figure}
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%
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% Brief description.
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% \begin{itemize}[nolistsep,topsep=0em,leftmargin=1pc]
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% \item The most interesting
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% \item and
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% \item methodology oriented
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% \item features
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% \item ideally with limited selection of citations
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% \end{itemize}
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% % Your favorite screenshot placed under ../pics/
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% % named as <project>_screenshot.png (optional numbers in suffixes if
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% % you have multiple to choose from)
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% \includegraphics[width=\columnwidth]{opensesame_screenshot1.png}
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% % Selected set of citations, Here is an example:
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% \ndcite{D. Geffroy, D. Rivière, I. Denghien, N. Souedet,
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% S. Laguitton, and Y. Cointepas. BrainVISA: a complete software
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% platform for neuroimaging. In Python in Neuroscience workshop,
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% Paris, Aug. 2011.}
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\ndproject{Neuroshare Tools}{http://g-node.org/neuroshare-tools}{neurosharelogo_square.png}{.2}{-0.25em}{0em}
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\item Comes with a tool to convert any data file supported by Neuroshare to the HDF5 format
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\end{itemize}
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\vspace{1em}
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%_________________________________neo_______________________________________
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\ndproject{Neo}{http://packages.python.org/neo/}{neo_logo.pdf}{.4}{-0.25em}{0em}
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Neo is a package for representing electrophysiology data in Python, together with support
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for reading a wide range of neurophysiology file formats, including Spike2, NeuroExplorer,
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AlphaOmega, Axon, Blackrock, Plexon, Tdt, and support for writing to a subset of these
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formats plus non-proprietary formats including HDF5.
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The goal of Neo is to improve interoperability between Python tools for analyzing, visualizing
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and generating electrophysiology data (such as OpenElectrophy, NeuroTools, G-node,
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Helmholtz, PyNN) by providing a common, shared object model. In order to be as
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lightweight a dependency as possible, Neo is deliberately limited to represention of data,
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with no functions for data analysis or visualization.
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Neo implements a hierarchical data model well adapted to intracellular and extracellular
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electrophysiology and EEG data with support for multi-electrodes (for example tetrodes).
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Neo's data objects build on the quantities package, which in turn builds on NumPy by
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adding support for physical dimensions. Thus Neo objects behave just like normal NumPy arrays,
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but with additional metadata, checks for dimensional consistency and automatic unit conversion.
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A project with similar aims but for neuroimaging file formats is NiBabel.
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\ndproject{Neo}{http://packages.python.org/neo}{neo_logo.pdf}{.4}{-0.25em}{-5em}
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Neo is a package provides a common model for representing
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electrophysiology data in Python. It provides I/O for reading a wide
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range of neurophysiology file formats, including Spike2,
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NeuroExplorer, AlphaOmega, Axon, Blackrock, Plexon, Tdt, and for
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writing to a subset of these formats plus non-proprietary formats
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including HDF5.
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% The goal of Neo is to improve interoperability between Python tools for analyzing, visualizing
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% and generating electrophysiology data (such as OpenElectrophy, NeuroTools, G-node,
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% Helmholtz, PyNN) by providing a common, shared object model. In order to be as
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% lightweight a dependency as possible, Neo is deliberately limited to represention of data,
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% with no functions for data analysis or visualization.
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Neo implements a hierarchical data model well adapted to intracellular
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and extracellular electrophysiology and EEG data with support for
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multi-electrodes (for example tetrodes). Neo's data objects build on
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the \href{http://pypi.python.org/pypi/quantities}{quantities} package,
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which in turn builds on \href{http://www.numpy.org}{NumPy} by adding
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support for physical dimensions. Thus Neo objects behave just like
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normal NumPy arrays, but with additional metadata, checks for
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dimensional consistency and automatic unit conversion.
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A project with similar aims but for neuroimaging file formats is
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\href{http://www.nipy.org/nibabel}{NiBabel}.
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%____________________OpenElectrophy______________________________________
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\ndproject{OpenElectrophy}{http://packages.python.org/OpenElectrophy/}{OpenElectrophy_logo.png}{.2}{-0.25em}{0em}
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%\ndproject{OpenElectrophy}{http://packages.python.org/OpenElectrophy/}{OpenElectrophy_logo.png}{.4}{-0.25em}{-5em}
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\ndproject{OpenElectrophy}{http://packages.python.org/OpenElectrophy}{OpenElectrophy_icon.png}{.18}{-0.25em}{0em}
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OpenElectrophy is build on top of neo.
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OpenElectrophy is build on top of neo. It provides
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\begin{itemize}[nolistsep,topsep=0em,leftmargin=1pc]
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\item A GUI on top of neo.
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\item A collection of method for spike sorting and powerfull GUI.
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\item A wavelet method for analysing transient oscillations in LFP.
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\item A customisable database to organize datasets.
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\item Powerfull GUI
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\item Collection of methods for spike sorting
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\item Wavelet method for analysing transient oscillations in LFP
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\item Customisable database to organize datasets
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\end{itemize}
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%___________________________________________________________________________
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formats and it is memory-efficient. Truely Open Source, BSD-licensed.
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\begin{itemize}[nolistsep,topsep=0em,leftmargin=1pc]
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\item user-friendly and customisable,
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\item interactive command-line interface in Python,
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\item graphical-user interface and visualization widgets,
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\item automatic and manual clustering,
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\item support for multi-channel data,
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\item based on: NumPy, PyTables, matplotlib, scikit-learn
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\item User-friendly and customisable
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\item Interactive command-line interface in Python
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\item GUI and visualization widgets
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\item Automatic and manual clustering
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\item Support for multi-channel data
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\item Based on: NumPy, PyTables, matplotlib, scikit-learn
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\end{itemize}
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%___________________________________________________________________________
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