The transform module provides infrastructure to transform a tree structure or a text to any datastructure you wish to create.

The basic idea of transformation is simple: for every Context in a tree structure, a method of a Transform instance is called. The method has the same name as the context’s lexicon, and is called with an Items instance containing the list of children of that context.

Sub-contexts in that list already have been replaced with the result of that context’s lexicon’s transformation method, wrapped in an Item, so the Items list consists of instances of either Token or Item. To make it easier to distinguish between the two, the Item class has an is_token class attribute, set to False.

Thus, a Transform class can closely mimic a corresponding Language class. If you want to ignore the output of a particular lexicon, don’t define a method with that name, but set its name to None in the Transform class definition.

How it works

The actual task of transformation (evaluation) is performed by a Transformer. The Transformer has infrastructure to choose the Transform class based on the current Language. Using the add_transform() method, you can assign a Transform instance to a Language class.

There are two convenience functions transform_text() and transform_tree().

For example:

from parce import root, Language, lexicon, default_action
from parce.action import Delimiter, Number, String
from parce.transform import Transform, transform_text

class MyLang(Language):
    def root(cls):
        yield r'\[', Delimiter, cls.list
        yield r'\d+', Number
        yield r'"', String, cls.string

    def list(cls):
        yield r'\]', Delimiter, -1
        yield from cls.root

    def string(cls):
        yield r'"', String, -1
        yield default_action, String

This language definition finds numbers, strings, and lists of those. We want to convert those to their Python equivalents. So, we create a corresponding Transform class, with methods having the same name as the lexicons in the Language definition:

class MyLangTransform(Transform):
    def root(self, items):
        result = []
        for i in items:
            if i.is_token:
                if i.action is Number:
                    result.append(int(i.text))  # a Number
                result.append(i.obj)            # a list or string
        return result

    def list(self, items):
        return self.root(items)

    def string(self, items):
        return items[0].text     # not the closing quote

Now let’s test our Transform!

>>> transform_text(MyLang.root, '1 2 3 [4 "Q" 6] x 7 8 9')
[1, 2, 3, [4, 'Q', 6], 7, 8, 9]

It works! The above function call is equivalent to:

>>> from parce.transform import Transformer
>>> t = Transformer()
>>> t.add_transform(MyLang, MyLangTransform())
>>> t.transform_text(MyLang.root, '1 2 3 [4 "Q" 6] x 7 8 9')
[1, 2, 3, [4, 'Q', 6], 7, 8, 9]

Transforming a tree structure

Using the same Transform class, you can also transform a tree structure:

>>> from parce.transform import transform_tree
>>> tree = root(MyLang.root, '1 2 3 [4 "Q" 6] x 7 8 9')
>>> tree.dump()
<Context MyLang.root at 0-23 (8 children)>
 ├╴<Token '1' at 0:1 (Literal.Number)>
 ├╴<Token '2' at 2:3 (Literal.Number)>
 ├╴<Token '3' at 4:5 (Literal.Number)>
 ├╴<Token '[' at 6:7 (Delimiter)>
 ├╴<Context MyLang.list at 7-15 (5 children)>
 │  ├╴<Token '4' at 7:8 (Literal.Number)>
 │  ├╴<Token '"' at 9:10 (Literal.String)>
 │  ├╴<Context MyLang.string at 10-12 (2 children)>
 │  │  ├╴<Token 'Q' at 10:11 (Literal.String)>
 │  │  ╰╴<Token '"' at 11:12 (Literal.String)>
 │  ├╴<Token '6' at 13:14 (Literal.Number)>
 │  ╰╴<Token ']' at 14:15 (Delimiter)>
 ├╴<Token '7' at 18:19 (Literal.Number)>
 ├╴<Token '8' at 20:21 (Literal.Number)>
 ╰╴<Token '9' at 22:23 (Literal.Number)>
>>> transform_tree(tree)
[1, 2, 3, [4, 'Q', 6], 7, 8, 9]

Note that the transform_tree() gets the root lexicon from the root element, and then automatically finds the corresponding Transform class, if you didn’t specify one yourself.

This is done by looking in the same module as the root lexicon’s language, and finding there a Transform subclass with the same name with "Transform" appended (see Transformer.find_transform()).

Examples of Transform classes can be found in the css and the json modules.

Calculator example

As a proof of concept, below is a simplistic calculator, it can be found in tests/

# Calculator parce example

from parce import Language, lexicon, default_target, skip
from parce.action import Number, Operator, Delimiter
from parce.transform import Transform

skip_whitespace = (r'\s+', skip)

class Calculator(Language):
    def root(cls):
        yield r'\d+', Number
        yield r'\-', Operator, cls.subtract
        yield r'\+', Operator, cls.add
        yield r'\*', Operator, cls.multiply
        yield r'/', Operator, cls.divide
        yield r'\(', Delimiter, cls.parens

    def parens(cls):
        yield r'\)', Delimiter, -1
        yield from cls.root

    def subtract(cls):
        yield r'\d+', Number
        yield r'\*', Operator, cls.multiply
        yield r'/', Operator, cls.divide
        yield r'\(', Delimiter, cls.parens
        yield skip_whitespace
        yield default_target, -1

    def add(cls):
        yield from cls.subtract

    def multiply(cls):
        yield r'\d+', Number
        yield r'\(', Delimiter, cls.parens
        yield skip_whitespace
        yield default_target, -1

    def divide(cls):
        yield from cls.multiply

class CalculatorTransform(Transform):
    def root(self, items):
        result = 0
        for i in items:
            if i.is_token:
                if i.action is Number:
                    result = int(i.text)
            elif == "add":
                result += i.obj
            elif == "subtract":
                result -= i.obj
            elif == "multiply":
                result *= i.obj
            elif == "divide":
                result /= i.obj
            else:   # == "parens":
                result = i.obj
        return result

    parens = add = subtract = multiply = divide = root

Test it with:

>>> from parce.transform import *
>>> transform_text(Calculator.root, " 1 + 1 ")
>>> transform_text(Calculator.root, " 1 + 2 * 3 ")
>>> transform_text(Calculator.root, " 1 * 2 + 3 ")
>>> transform_text(Calculator.root, " (1 + 2) * 3 ")

Integration with TreeBuilder and Document

It is very easy to keep a transformed structure up-to-date when a tree changes. The Transformer caches the result of every transform method using a weak reference to the Context that yielded that result.

When the TreeBuilder changes the tree, it emits the event "invalidate" with the youngest node that has its children changed (i.e. tokens or contexts were added or removed).

The Transformer then knows that that context and all its ancestors need to be recomputed. In the process all newly added contexts are evaluated as well, because their transformations can’t be found in the cache. Contexts that only changed position are not recomputed. If you want your transformed structure to know the position in the text, you should store references to the corresponding tokens in your structure. The pos attribute of the Tokens that move is adjusted by the tree builder, so they still point to the right position after an update of the tree.

Here is an example:

>>> from parce.lang.json import Json
>>> from parce import Document
>>> from parce.transform import Transformer
>>> d = Document(Json.root)
>>> t = Transformer()
>>> t.connect_treebuilder(d.builder())
>>> d.set_text('{"key": [1, 2, 3, 4, 5]}')
>>> t.result(d.get_root())
{'key': [1, 2, 3, 4, 5]}
>>> d.insert(22, ", 6, 7, 8")
>>> t.result(d.get_root())
{'key': [1, 2, 3, 4, 5, 6, 7, 8]}

Note that after inserting some text the transformed result automatically gets updated, because we called the connect_treebuilder() method.

When the tree builder is about to inject the modified tree part in the Document’s tree, it emits the "replace" event. The transformer reacts by interrupting any current job that might be busy computing the transformed result. When the tree builder is replacing, it emits the "invalidate" event, so the transformer knows which contexts need to be deleted from the cache. Finally, when the tree builder emits "finished" the transformer rebuilds our transformed result, using as much as possible the previously cached transform results for Contexts that did not change.

A single Transformer can be used for multiple transformation jobs for multiple documents or tree builders, even at the same time. It shares the added Transform instances between multiple jobs and documents. If your Transform classes keep internal state that might not be desirable; in that case you can use a Transformer for every document or tree.

If you were using a BackgroundTreeBuilder, the transformed result is automatically computed in a background thread as well (during the self.emit("finished") call in the tree builder’s method process_finished()). But you can also use a BackgroundTransformer or inherit from Transformer to add your own implementation for running in the background.