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try:
from future_builtins import filter
except ImportError:
pass
from copy import deepcopy
###{standalone
from collections import OrderedDict
class Meta:
def __init__(self):
self.empty = True
class Tree(object):
"""The main tree class.
Creates a new tree, and stores "data" and "children" in attributes of the same name.
Trees can be hashed and compared.
Parameters:
data: The name of the rule or alias
children: List of matched sub-rules and terminals
meta: Line & Column numbers (if ``propagate_positions`` is enabled).
meta attributes: line, column, start_pos, end_line, end_column, end_pos
"""
def __init__(self, data, children, meta=None):
self.data = data
self.children = children
self._meta = meta
@property
def meta(self):
if self._meta is None:
self._meta = Meta()
return self._meta
def __repr__(self):
return 'Tree(%r, %r)' % (self.data, self.children)
def _pretty_label(self):
return self.data
def _pretty(self, level, indent_str):
if len(self.children) == 1 and not isinstance(self.children[0], Tree):
return [indent_str*level, self._pretty_label(), '\t', '%s' % (self.children[0],), '\n']
l = [indent_str*level, self._pretty_label(), '\n']
for n in self.children:
if isinstance(n, Tree):
l += n._pretty(level+1, indent_str)
else:
l += [indent_str*(level+1), '%s' % (n,), '\n']
return l
def pretty(self, indent_str=' '):
"""Returns an indented string representation of the tree.
Great for debugging.
"""
return ''.join(self._pretty(0, indent_str))
def __eq__(self, other):
try:
return self.data == other.data and self.children == other.children
except AttributeError:
return False
def __ne__(self, other):
return not (self == other)
def __hash__(self):
return hash((self.data, tuple(self.children)))
def iter_subtrees(self):
"""Depth-first iteration.
Iterates over all the subtrees, never returning to the same node twice (Lark's parse-tree is actually a DAG).
"""
queue = [self]
subtrees = OrderedDict()
for subtree in queue:
subtrees[id(subtree)] = subtree
queue += [c for c in reversed(subtree.children)
if isinstance(c, Tree) and id(c) not in subtrees]
del queue
return reversed(list(subtrees.values()))
def find_pred(self, pred):
"""Returns all nodes of the tree that evaluate pred(node) as true."""
return filter(pred, self.iter_subtrees())
def find_data(self, data):
"""Returns all nodes of the tree whose data equals the given data."""
return self.find_pred(lambda t: t.data == data)
###}
def expand_kids_by_index(self, *indices):
"""Expand (inline) children at the given indices"""
for i in sorted(indices, reverse=True): # reverse so that changing tail won't affect indices
kid = self.children[i]
self.children[i:i+1] = kid.children
def expand_kids_by_data(self, *data_values):
"""Expand (inline) children with any of the given data values. Returns True if anything changed"""
changed = False
for i in range(len(self.children)-1, -1, -1):
child = self.children[i]
if isinstance(child, Tree) and child.data in data_values:
self.children[i:i+1] = child.children
changed = True
return changed
def scan_values(self, pred):
"""Return all values in the tree that evaluate pred(value) as true.
This can be used to find all the tokens in the tree.
Example:
>>> all_tokens = tree.scan_values(lambda v: isinstance(v, Token))
"""
for c in self.children:
if isinstance(c, Tree):
for t in c.scan_values(pred):
yield t
else:
if pred(c):
yield c
def iter_subtrees_topdown(self):
"""Breadth-first iteration.
Iterates over all the subtrees, return nodes in order like pretty() does.
"""
stack = [self]
while stack:
node = stack.pop()
if not isinstance(node, Tree):
continue
yield node
for n in reversed(node.children):
stack.append(n)
def __deepcopy__(self, memo):
return type(self)(self.data, deepcopy(self.children, memo), meta=self._meta)
def copy(self):
return type(self)(self.data, self.children)
def set(self, data, children):
self.data = data
self.children = children
# XXX Deprecated! Here for backwards compatibility <0.6.0
@property
def line(self):
return self.meta.line
@property
def column(self):
return self.meta.column
@property
def end_line(self):
return self.meta.end_line
@property
def end_column(self):
return self.meta.end_column
class SlottedTree(Tree):
__slots__ = 'data', 'children', 'rule', '_meta'
def pydot__tree_to_png(tree, filename, rankdir="LR", **kwargs):
graph = pydot__tree_to_graph(tree, rankdir, **kwargs)
graph.write_png(filename)
def pydot__tree_to_dot(tree, filename, rankdir="LR", **kwargs):
graph = pydot__tree_to_graph(tree, rankdir, **kwargs)
graph.write(filename)
def pydot__tree_to_graph(tree, rankdir="LR", **kwargs):
"""Creates a colorful image that represents the tree (data+children, without meta)
Possible values for `rankdir` are "TB", "LR", "BT", "RL", corresponding to
directed graphs drawn from top to bottom, from left to right, from bottom to
top, and from right to left, respectively.
`kwargs` can be any graph attribute (e. g. `dpi=200`). For a list of
possible attributes, see https://www.graphviz.org/doc/info/attrs.html.
"""
import pydot
graph = pydot.Dot(graph_type='digraph', rankdir=rankdir, **kwargs)
i = [0]
def new_leaf(leaf):
node = pydot.Node(i[0], label=repr(leaf))
i[0] += 1
graph.add_node(node)
return node
def _to_pydot(subtree):
color = hash(subtree.data) & 0xffffff
color |= 0x808080
subnodes = [_to_pydot(child) if isinstance(child, Tree) else new_leaf(child)
for child in subtree.children]
node = pydot.Node(i[0], style="filled", fillcolor="#%x" % color, label=subtree.data)
i[0] += 1
graph.add_node(node)
for subnode in subnodes:
graph.add_edge(pydot.Edge(node, subnode))
return node
_to_pydot(tree)
return graph
|
