\HeaderA{sanger.read.spikes}{Read in a nexTimestamps file from Neuroexplorer and create
a "spikes" data structure.}{sanger.read.spikes}
\begin{Description}\relax
The Genes to Cognition Project (G2C) uses Neuroexplorer to output spike
times from an MEA recording.  This function reads those text files and
creates a "spikes" data structure.
\end{Description}
\begin{Usage}
\begin{verbatim}
sanger.read.spikes(filename)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{filename}] Name of the text file to be read in.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
This format is fairly simple; each column represents one channel, with
the spikes recorded in that channel filling up the column.  Since
different channels are likely to record different numbers of spikes,
empty values (just a TAB to next value) are found at the bottom of
each spike train.

Text files can be compressed, using gzip, to save space.  (The
extension .gz is added to compresed files.)  This compression aspect
should be transparent to the end user -- if a file is compresed, the
filename can be given with or without the .gz extension.  gzip is
available for Windows (from \url{http://www.gzip.org}); "gzip
*.nexTimestamps" will compress all timestamps, and "gzip -d
*.nexTimestamps" will uncompress them.
\end{Details}
\begin{Value}
Return the data structure 's'.
\end{Value}
\begin{Author}\relax
Stephen Eglen
\end{Author}
\begin{References}\relax
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{jay.read.spikes}{jay.read.spikes}}
\code{\LinkA{compute.ns}{compute.ns}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
data.file <- system.file("examples", "TC89_DIV15_A.nexTimestamps",
                         package = "sjemea")
s <- sanger.read.spikes( data.file, beg=400, end=700)
fourplot(s)
\end{ExampleCode}
\end{Examples}

