structure of library/tensor shaping up

doc/library/tensor/basic.txt

+
+.. currentmodule:: tensor
+
+TensorType
+==========
+
+.. class:: TensorType
+
+   .. method:: quux()
+
+
+Creation
+========
+
+Autocasting
+-----------
+
+TODO: What does (or compatible) mean?  Talk about casting rules, refer .
+
+
+.. function:: as_tensor_variable(x, ...)
+
+    TODO: Link to 'autocasting'
+
+
+.. function:: lvector(name=None)
+
+TODO: make a table of all [scalar, vector, matrix, tensor3, tensor4] vs. [b,
+w, i, l, f, d, c, z]
+
+
+Shaping and Shuffling
+=====================
+
+.. function:: shape(x)
+
+    :param x:  symbolic Tensor (or compatible)
+
+    Returns the symbolic shape vector of `x`
+
+.. function:: reshape(x)
+
+.. function:: dimshuffle(x)
+
+
+Reductions 
+==========
+
+
+.. function:: max(x)
+
+    :param x:  symbolic Tensor (or compatible)
+
+    Returns TODO
+
+.. function:: min(x)
+
+    :param x:  symbolic Tensor (or compatible)
+
+    Returns TODO
+
+.. function:: sum(x)
+
+    :param x:  symbolic Tensor (or compatible)
+
+    Returns TODO
+
+Indexing
+========
+
+Basic indexing.
+
+Advanced indexing.
+
+Elementwise
+===========
+
+Casting
+-------
+
+Logic Functions
+---------------
+
+Mathematical
+------------
+
+Broadcasting in Theano vs. Numpy
+--------------------------------
+
+Linear Algebra
+==============
+

doc/library/tensor/index.txt

+==================================================
+:mod:`tensor`  -- types and ops for symbolic numpy
+==================================================
 
-.. _libdoc_tensor:
-
-============================================================
-:mod:`tensor` -- types and ops for symbolic numpy [doc TODO]
-============================================================
-
-.. module:: theano.tensor
+.. module:: tensor
    :platform: Unix, Windows
    :synopsis: symbolic types and operations for n-dimensional arrays.
 .. moduleauthor:: LISA
 
-TODO: What does (or compatible) mean?  Talk about casting rules, refer .
-
-TensorType
-==========
-
-.. class:: TensorType
-
-   .. method:: quux()
-
-
-Creation
-========
-
-Autocasting
------------
-
-.. function:: as_tensor_variable(x, ...)
-
-    TODO: Link to 'autocasting'
-
-
-.. function:: lvector(name=None)
-
-TODO: make a table of all [scalar, vector, matrix, tensor3, tensor4] vs. [b,
-w, i, l, f, d, c, z]
-
-
-Shaping and Shuffling
-=====================
-
-.. function:: shape(x)
-
-    :param x:  symbolic Tensor (or compatible)
-
-    Returns the symbolic shape vector of `x`
-
-.. function:: reshape(x)
-
-.. function:: dimshuffle(x)
-
-
-Reductions 
-==========
-
-
-.. function:: max(x)
-
-    :param x:  symbolic Tensor (or compatible)
-
-    Returns TODO
-
-.. function:: min(x)
-
-    :param x:  symbolic Tensor (or compatible)
-
-    Returns TODO
-
-.. function:: sum(x)
-
-    :param x:  symbolic Tensor (or compatible)
-
-    Returns TODO
-
-Indexing
-========
-
-Basic indexing.
-
-Advanced indexing.
-
-Elementwise
-===========
-
-Casting
--------
-
-Logic Functions
----------------
-
-Mathematical
-------------
-
-Broadcasting in Theano vs. Numpy
---------------------------------
-
-Random Sampling
-===============
-
-See :ref:`libdoc_tensor_shared_randomstreams`.
-
-Linear Algebra
-==============
-
-Fourier Transforms
-==================
-
-[James has some code for this, but hasn't gotten it into the source tree yet.]
+Theano's strength is in expressing symbolic calculations involving tensors.  
+There are many types of symbolic expressions for tensors.  For everyone's
+sanity, they are grouped into the following sections:
 
+.. toctree::
+    :maxdepth: 1
 
+    basic
+    shared_randomstreams