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!
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 IPython.core.inputtransformer.InputTransformer
, and are used by IPython.core.inputsplitter.IPythonInputSplitter
.
These transformers act in three groups, stored separately as lists of instances in attributes of ~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 ~IPython.core.inputsplitter.IPythonInputSplitter
instances, as the attributes ~IPython.core.interactiveshell.InteractiveShell.input_splitter
, to tell when a block of input is complete, and ~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, e.g.:
ip.input_splitter.logical_line_transforms.append(my_transformer())
ip.input_transformer_manager.logical_line_transforms.append(my_transformer())
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.
2.0
Added the option to raise SyntaxError
.
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
:
@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 ~IPython.core.inputtransformer.StatelessInputTransformer
using your function.
Warning
Transforming a full block at once will break the automatic detection of whether a block of code is complete in interfaces relying on this functionality, such as terminal IPython. You will need to use a shortcut to force-execute your cells.
Transforming a full block of python code is possible by implementing a ~IPython.core.inputtransformer.Inputtransformer
and overwriting the push
and reset
methods. The reset method should send the full block of transformed text. As an example a transformer the reversed the lines from last to first.
from IPython.core.inputtransformer import InputTransformer
class ReverseLineTransformer(InputTransformer):
- def __init__(self):
self.acc = []
- def push(self, line):
self.acc.append(line) return None
- def reset(self):
ret = 'n'.join(self.acc[::-1]) self.acc = [] return ret
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.
You may use CoroutineInputTransformer.wrap
to simplify the creation of such a transformer.
Here is a simple CoroutineInputTransformer
that can be thought of being the identity:
from IPython.core.inputtransformer import CoroutineInputTransformer
@CoroutineInputTransformer.wrap
def noop():
line = ''
while True:
line = (yield line)
ip = get_ipython()
ip.input_splitter.logical_line_transforms.append(noop())
ip.input_transformer_manager.logical_line_transforms.append(noop())
This code in IPython strips a constant amount of leading indentation from each line in a cell:
from IPython.core.inputtransformer import CoroutineInputTransformer
@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)
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 python_line_transforms
.
After the code has been parsed as Python syntax, you can use Python's powerful Abstract Syntax Tree tools to modify it. Subclass 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