--- trunk/doc/report/types.tex 2010/05/28 15:16:21 101
+++ trunk/doc/report/types.tex 2010/07/20 14:08:09 161
@@ -30,8 +30,25 @@
\end{center}%
\section{Images}
+Images are rectangular arrays of tensor data that are used to represent the data sets
+that Diderot programs are analysing, as well as other data.
+The syntax of an image type is
+\begin{center}
+ \kw{image}\kw{(} $n$ \kw{)} \kw{[} $d_1,\ldots{},d_n$ \kw{]}
+\end{center}%
+where $n$ is the dimension of the field (typically 2 or 3) and $d_1,\ldots{},d_n$ is the
+shape of the tensor data (\ie{}, the elements of the image are tensors
+of type \kw{tensor[}$d_1,\ldots{},d_n$\kw{]}).
\section{Fields}
+Fields are functions from some $n$-dimensional vector space to some tensor type.
+The syntax of a field type is
+\begin{center}
+ \kw{field}\kw{\#} $k$ \kw{(} $n$ \kw{)} \kw{[} $d_1,\ldots{},d_n$ \kw{]}
+\end{center}%
+where $k \geq 0$ is the number of levels of differentiation supported by the field,
+$n$ is the dimension of the field (typically 2 or 3), and $d_1,\ldots{},d_n$ is the shape of the field.
+Probing the field will produce a tensor of type \kw{tensor[}$d_1,\ldots{},d_n$\kw{]}.
\section{Kernels}
Kernels are abstract types that represent the \emph{convolution kernels} used