Custom input transformation¶
IPython extends Python syntax to allow things like magic commands, and help with
? 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¶
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 act in three groups, stored separately as lists of instances
in attributes of
physical_line_transformsact on the lines as the user enters them. For example, these strip Python prompts from examples pasted in.
logical_line_transformsact 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
%magiccommands, for instance.
python_line_transformsact 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
IPythonInputSplitter instances, as the
to tell when a block of input is complete, and
to transform complete cells. If you add a transformer, you should make sure that
it gets added to both, e.g.:
These transformers may raise
SyntaxError if the input code is invalid, but
in most cases it is clearer to pass unrecognised code through unmodified and let
Python’s own parser decide whether it is valid.
Changed in version 2.0: Added the option to raise
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
@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
StatelessInputTransformer using your
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
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
TokenInputTransformer.wrap(). These should only be used in
After the code has been parsed as Python syntax, you can use Python’s powerful
Abstract Syntax Tree tools to modify it. Subclass
and add an instance to
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