
===========================
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`.

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.

An InteractiveShell instance actually has two
:class:`~IPython.core.inputsplitter.IPythonInputSplitter` instances, as the
attributes :attr:`~IPython.core.interactiveshell.InteractiveShell.input_splitter`,
to tell when a block of input is complete, and
:attr:`~IPython.core.interactiveshell.InteractiveShell.input_transformer_manager`,
to transform complete cells. If you add a transformer, you should make sure that
it gets added to both.

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:`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
