Improve input transformation docs

docs/source/config/inputtransforms.txt

@@ -1,103 +1,121 b''
 
 ===========================
 Custom input transformation
 ===========================
 
 IPython extends Python syntax to allow things like magic commands, and help with
 the ``?`` syntax. There are several ways to customise how the user's input is
 processed into Python code to be executed.
 
 These hooks are mainly for other projects using IPython as the core of their
 interactive interface. Using them carelessly can easily break IPython!
 
 String based transformations
 ============================
 
+.. currentmodule:: IPython.core.inputtransforms
+
 When the user enters a line of code, it is first processed as a string. By the
 end of this stage, it must be valid Python syntax.
 
 These transformers all subclass :class:`IPython.core.inputtransformer.InputTransformer`,
-and are used by :class:`IPython.core.inputsplitter.IPythonInputSplitter`,
-the ``transform`` attribute of which is a list of instances.
-
-By default, these transformers are skipped when :class:`~IPython.core.inputsplitter.IPythonInputSplitter`
-detects that the line starts inside a multi-line string. Some transformers, such
-as those that remove the prompt markers from pasted examples, need to look
-inside multiline strings as well - these set the attribute ``look_in_string`` to
-``True``.
+and are used by :class:`IPython.core.inputsplitter.IPythonInputSplitter`.
+
+These transformers act in three groups, stored separately as lists of instances
+in attributes of :class:`~IPython.core.inputsplitter.IPythonInputSplitter`:
+
+* ``physical_line_transforms`` act on the lines as the user enters them. For
+  example, these strip Python prompts from examples pasted in.
+* ``logical_line_transforms`` act on lines as connected by explicit line
+  continuations, i.e. ``\`` at the end of physical lines. They are skipped
+  inside multiline Python statements. This is the point where IPython recognises
+  ``%magic`` commands, for instance.
+* ``python_line_transforms`` act on blocks containing complete Python statements.
+  Multi-line strings, lists and function calls are reassembled before being
+  passed to these, but note that function and class *definitions* are still a
+  series of separate statements. IPython does not use any of these by default.
 
 Stateless transformations
 -------------------------
 
 The simplest kind of transformations work one line at a time. Write a function
 which takes a line and returns a line, and decorate it with
-:meth:`~IPython.core.inputtransformer.StatelessInputTransformer.wrap`::
+:meth:`StatelessInputTransformer.wrap`::
 
     @StatelessInputTransformer.wrap
     def my_special_commands(line):
         if line.startswith("¬"):
             return "specialcommand(" + repr(line) + ")"
         return line
 
 The decorator returns a factory function which will produce instances of
 :class:`~IPython.core.inputtransformer.StatelessInputTransformer` using your
 function.
 
 Coroutine transformers
 ----------------------
 
 More advanced transformers can be written as coroutines. The coroutine will be
 sent each line in turn, followed by ``None`` to reset it. It can yield lines, or
 ``None`` if it is accumulating text to yield at a later point. When reset, it
 should give up any code it has accumulated.
 
 This code in IPython strips a constant amount of leading indentation from each
 line in a cell::
 
     @CoroutineInputTransformer.wrap
     def leading_indent():
         """Remove leading indentation.
         
         If the first line starts with a spaces or tabs, the same whitespace will be
         removed from each following line until it is reset.
         """
         space_re = re.compile(r'^[ \t]+')
         line = ''
         while True:
             line = (yield line)
             
             if line is None:
                 continue
             
             m = space_re.match(line)
             if m:
                 space = m.group(0)
                 while line is not None:
                     if line.startswith(space):
                         line = line[len(space):]
                     line = (yield line)
             else:
                 # No leading spaces - wait for reset
                 while line is not None:
                     line = (yield line)
 
     leading_indent.look_in_string = True
 
+Token-based transformers
+------------------------
+
+There is an experimental framework that takes care of tokenizing and
+untokenizing lines of code. Define a function that accepts a list of tokens, and
+returns an iterable of output tokens, and decorate it with
+:meth:`TokenInputTransformer.wrap`. These should only be used in
+``python_line_transforms``.
+
 AST transformations
 ===================
 
 After the code has been parsed as Python syntax, you can use Python's powerful
 *Abstract Syntax Tree* tools to modify it. Subclass :class:`ast.NodeTransformer`,
 and add an instance to ``shell.ast_transformers``.
 
 This example wraps integer literals in an ``Integer`` class, which is useful for
 mathematical frameworks that want to handle e.g. ``1/3`` as a precise fraction::
 
 
     class IntegerWrapper(ast.NodeTransformer):
         """Wraps all integers in a call to Integer()"""
         def visit_Num(self, node):
             if isinstance(node.n, int):
                 return ast.Call(func=ast.Name(id='Integer', ctx=ast.Load()),
                                 args=[node], keywords=[])
             return node