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📄 __init__.py(2.48 KB)
📄 __init__.pyc(3.99 KB)
📄 __init__.pyo(3.99 KB)
📄 _collections.py(27.24 KB)
📄 _collections.pyc(47.87 KB)
📄 _collections.pyo(47.87 KB)
📄 compat.py(6.75 KB)
📄 compat.pyc(9.24 KB)
📄 compat.pyo(9.18 KB)
📄 deprecations.py(4.3 KB)
📄 deprecations.pyc(5.65 KB)
📄 deprecations.pyo(5.65 KB)
📄 langhelpers.py(41.63 KB)
📄 langhelpers.pyc(52.34 KB)
📄 langhelpers.pyo(52.28 KB)
📄 queue.py(6.39 KB)
📄 queue.pyc(7.21 KB)
📄 queue.pyo(7.21 KB)
📄 topological.py(2.73 KB)
📄 topological.pyc(2.41 KB)
📄 topological.pyo(2.41 KB)

Editing: topological.py

# util/topological.py
# Copyright (C) 2005-2017 the SQLAlchemy authors and contributors
# <see AUTHORS file>
#
# This module is part of SQLAlchemy and is released under
# the MIT License: http://www.opensource.org/licenses/mit-license.php

"""Topological sorting algorithms."""

from ..exc import CircularDependencyError
from .. import util

__all__ = ['sort', 'sort_as_subsets', 'find_cycles']

def sort_as_subsets(tuples, allitems, deterministic_order=False):

edges = util.defaultdict(set)
    for parent, child in tuples:
        edges[child].add(parent)

Set = util.OrderedSet if deterministic_order else set

todo = Set(allitems)

while todo:
        output = Set()
        for node in todo:
            if todo.isdisjoint(edges[node]):
                output.add(node)

if not output:
            raise CircularDependencyError(
                "Circular dependency detected.",
                find_cycles(tuples, allitems),
                _gen_edges(edges)
            )

todo.difference_update(output)
        yield output

def sort(tuples, allitems, deterministic_order=False):
    """sort the given list of items by dependency.

'tuples' is a list of tuples representing a partial ordering.
    'deterministic_order' keeps items within a dependency tier in list order.
    """

for set_ in sort_as_subsets(tuples, allitems, deterministic_order):
        for s in set_:
            yield s

def find_cycles(tuples, allitems):
    # adapted from:
    # http://neopythonic.blogspot.com/2009/01/detecting-cycles-in-directed-graph.html

edges = util.defaultdict(set)
    for parent, child in tuples:
        edges[parent].add(child)
    nodes_to_test = set(edges)

output = set()

# we'd like to find all nodes that are
    # involved in cycles, so we do the full
    # pass through the whole thing for each
    # node in the original list.

# we can go just through parent edge nodes.
    # if a node is only a child and never a parent,
    # by definition it can't be part of a cycle.  same
    # if it's not in the edges at all.
    for node in nodes_to_test:
        stack = [node]
        todo = nodes_to_test.difference(stack)
        while stack:
            top = stack[-1]
            for node in edges[top]:
                if node in stack:
                    cyc = stack[stack.index(node):]
                    todo.difference_update(cyc)
                    output.update(cyc)

if node in todo:
                    stack.append(node)
                    todo.remove(node)
                    break
            else:
                node = stack.pop()
    return output

def _gen_edges(edges):
    return set([
        (right, left)
        for left in edges
        for right in edges[left]
    ])

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