| Current File : /home/inlingua/miniconda3/lib/python3.1/site-packages/conda/models/prefix_graph.py |
# Copyright (C) 2012 Anaconda, Inc
# SPDX-License-Identifier: BSD-3-Clause
"""Implements directed graphs to sort and manipulate packages within a prefix.
Object inheritance:
.. autoapi-inheritance-diagram:: PrefixGraph GeneralGraph
:top-classes: conda.models.prefix_graph.PrefixGraph
:parts: 1
"""
from collections import defaultdict
from logging import getLogger
from boltons.setutils import IndexedSet
from ..base.context import context
from ..common.compat import on_win
from ..exceptions import CyclicalDependencyError
from .enums import NoarchType
from .match_spec import MatchSpec
log = getLogger(__name__)
class PrefixGraph:
"""
A directed graph structure used for sorting packages (prefix_records) in prefixes and
manipulating packages within prefixes (e.g. removing and pruning).
The terminology used for edge direction is "parents" and "children" rather than "successors"
and "predecessors". The parent nodes of a record are those records in the graph that
match the record's "depends" field. E.g. NodeA depends on NodeB, then NodeA is a child
of NodeB, and NodeB is a parent of NodeA. Nodes can have zero parents, or more than two
parents.
Most public methods mutate the graph.
"""
def __init__(self, records, specs=()):
records = tuple(records)
specs = set(specs)
self.graph = graph = {} # dict[PrefixRecord, set[PrefixRecord]]
self.spec_matches = spec_matches = {} # dict[PrefixRecord, set[MatchSpec]]
for node in records:
parent_match_specs = tuple(MatchSpec(d) for d in node.depends)
parent_nodes = {
rec for rec in records if any(m.match(rec) for m in parent_match_specs)
}
graph[node] = parent_nodes
matching_specs = IndexedSet(s for s in specs if s.match(node))
if matching_specs:
spec_matches[node] = matching_specs
self._toposort()
def remove_spec(self, spec):
"""
Remove all matching nodes, and any associated child nodes.
Args:
spec (MatchSpec):
Returns:
tuple[PrefixRecord]: The removed nodes.
"""
node_matches = {node for node in self.graph if spec.match(node)}
# If the spec was a track_features spec, then we need to also remove every
# package with a feature that matches the track_feature.
for feature_name in spec.get_raw_value("track_features") or ():
feature_spec = MatchSpec(features=feature_name)
node_matches.update(node for node in self.graph if feature_spec.match(node))
remove_these = set()
for node in node_matches:
remove_these.add(node)
remove_these.update(self.all_descendants(node))
remove_these = tuple(filter(lambda node: node in remove_these, self.graph))
for node in remove_these:
self._remove_node(node)
self._toposort()
return tuple(remove_these)
def remove_youngest_descendant_nodes_with_specs(self):
"""
A specialized method used to determine only dependencies of requested specs.
Returns:
tuple[PrefixRecord]: The removed nodes.
"""
graph = self.graph
spec_matches = self.spec_matches
inverted_graph = {
node: {key for key in graph if node in graph[key]} for node in graph
}
youngest_nodes_with_specs = tuple(
node
for node, children in inverted_graph.items()
if not children and node in spec_matches
)
removed_nodes = tuple(
filter(lambda node: node in youngest_nodes_with_specs, self.graph)
)
for node in removed_nodes:
self._remove_node(node)
self._toposort()
return removed_nodes
@property
def records(self):
return iter(self.graph)
def prune(self):
"""Prune back all packages until all child nodes are anchored by a spec.
Returns:
tuple[PrefixRecord]: The pruned nodes.
"""
graph = self.graph
spec_matches = self.spec_matches
original_order = tuple(self.graph)
removed_nodes = set()
while True:
inverted_graph = {
node: {key for key in graph if node in graph[key]} for node in graph
}
prunable_nodes = tuple(
node
for node, children in inverted_graph.items()
if not children and node not in spec_matches
)
if not prunable_nodes:
break
for node in prunable_nodes:
removed_nodes.add(node)
self._remove_node(node)
removed_nodes = tuple(
filter(lambda node: node in removed_nodes, original_order)
)
self._toposort()
return removed_nodes
def get_node_by_name(self, name):
return next(rec for rec in self.graph if rec.name == name)
def all_descendants(self, node):
graph = self.graph
inverted_graph = {
node: {key for key in graph if node in graph[key]} for node in graph
}
nodes = [node]
nodes_seen = set()
q = 0
while q < len(nodes):
for child_node in inverted_graph[nodes[q]]:
if child_node not in nodes_seen:
nodes_seen.add(child_node)
nodes.append(child_node)
q += 1
return tuple(filter(lambda node: node in nodes_seen, graph))
def all_ancestors(self, node):
graph = self.graph
nodes = [node]
nodes_seen = set()
q = 0
while q < len(nodes):
for parent_node in graph[nodes[q]]:
if parent_node not in nodes_seen:
nodes_seen.add(parent_node)
nodes.append(parent_node)
q += 1
return tuple(filter(lambda node: node in nodes_seen, graph))
def _remove_node(self, node):
"""Removes this node and all edges referencing it."""
graph = self.graph
if node not in graph:
raise KeyError(f"node {node} does not exist")
graph.pop(node)
self.spec_matches.pop(node, None)
for node, edges in graph.items():
if node in edges:
edges.remove(node)
def _toposort(self):
graph_copy = {node: IndexedSet(parents) for node, parents in self.graph.items()}
self._toposort_prepare_graph(graph_copy)
if context.allow_cycles:
sorted_nodes = tuple(self._topo_sort_handle_cycles(graph_copy))
else:
sorted_nodes = tuple(self._toposort_raise_on_cycles(graph_copy))
original_graph = self.graph
self.graph = {node: original_graph[node] for node in sorted_nodes}
return sorted_nodes
@classmethod
def _toposort_raise_on_cycles(cls, graph):
if not graph:
return
while True:
no_parent_nodes = IndexedSet(
sorted(
(node for node, parents in graph.items() if len(parents) == 0),
key=lambda x: x.name,
)
)
if not no_parent_nodes:
break
for node in no_parent_nodes:
yield node
graph.pop(node, None)
for parents in graph.values():
parents -= no_parent_nodes
if len(graph) != 0:
raise CyclicalDependencyError(tuple(graph))
@classmethod
def _topo_sort_handle_cycles(cls, graph):
# remove edges that point directly back to the node
for k, v in graph.items():
v.discard(k)
# disconnected nodes go first
nodes_that_are_parents = {
node for parents in graph.values() for node in parents
}
nodes_without_parents = (node for node in graph if not graph[node])
disconnected_nodes = sorted(
(
node
for node in nodes_without_parents
if node not in nodes_that_are_parents
),
key=lambda x: x.name,
)
yield from disconnected_nodes
t = cls._toposort_raise_on_cycles(graph)
while True:
try:
value = next(t)
yield value
except CyclicalDependencyError as e:
# TODO: Turn this into a warning, but without being too annoying with
# multiple messages. See https://github.com/conda/conda/issues/4067
log.debug("%r", e)
yield cls._toposort_pop_key(graph)
t = cls._toposort_raise_on_cycles(graph)
continue
except StopIteration:
return
@staticmethod
def _toposort_pop_key(graph):
"""
Pop an item from the graph that has the fewest parents.
In the case of a tie, use the node with the alphabetically-first package name.
"""
node_with_fewest_parents = sorted(
(len(parents), node.dist_str(), node) for node, parents in graph.items()
)[0][2]
graph.pop(node_with_fewest_parents)
for parents in graph.values():
parents.discard(node_with_fewest_parents)
return node_with_fewest_parents
@staticmethod
def _toposort_prepare_graph(graph):
# There are currently at least three special cases to be aware of.
# 1. Remove any circular dependency between python and pip. This typically comes about
# because of the add_pip_as_python_dependency configuration parameter.
for node in graph:
if node.name == "python":
parents = graph[node]
for parent in tuple(parents):
if parent.name == "pip":
parents.remove(parent)
# 2. Special case code for menuinst.
# Always link/unlink menuinst first/last in case a subsequent
# package tries to import it to create/remove a shortcut.
menuinst_node = next((node for node in graph if node.name == "menuinst"), None)
python_node = next((node for node in graph if node.name == "python"), None)
if menuinst_node:
# add menuinst as a parent if python is a parent and the node
# isn't a parent of menuinst
assert python_node is not None
menuinst_parents = graph[menuinst_node]
for node, parents in graph.items():
if python_node in parents and node not in menuinst_parents:
parents.add(menuinst_node)
if on_win:
# 3. On windows, python noarch packages need an implicit dependency on conda added, if
# conda is in the list of packages for the environment. Python noarch packages
# that have entry points use conda's own conda.exe python entry point binary. If
# conda is going to be updated during an operation, the unlink / link order matters.
# See issue #6057.
conda_node = next((node for node in graph if node.name == "conda"), None)
if conda_node:
# add conda as a parent if python is a parent and node isn't a parent of conda
conda_parents = graph[conda_node]
for node, parents in graph.items():
if (
hasattr(node, "noarch")
and node.noarch == NoarchType.python
and node not in conda_parents
):
parents.add(conda_node)
# def dot_repr(self, title=None): # pragma: no cover
# # graphviz DOT graph description language
#
# builder = ['digraph g {']
# if title:
# builder.append(' labelloc="t";')
# builder.append(' label="%s";' % title)
# builder.append(' size="10.5,8";')
# builder.append(' rankdir=BT;')
# for node in self.get_nodes_ordered_from_roots():
# label = "%s %s" % (node.record.name, node.record.version)
# if node.specs:
# # TODO: combine?
# spec = next(iter(node.specs))
# label += "\\n%s" % ("?%s" if spec.optional else "%s") % spec
# if node.is_orphan:
# shape = "box"
# elif node.is_root:
# shape = "invhouse"
# elif node.is_leaf:
# shape = "house"
# else:
# shape = "ellipse"
# builder.append(' "%s" [label="%s", shape=%s];' % (node.record.name, label, shape))
# for child in node.required_children:
# builder.append(' "%s" -> "%s";' % (child.record.name, node.record.name))
# for child in node.optional_children:
# builder.append(' "%s -> "%s" [color=lightgray];' % (child.record.name,
# node.record.name))
# builder.append('}')
# return '\n'.join(builder)
#
# def format_url(self): # pragma: no cover
# return "https://condaviz.glitch.me/%s" % url_quote(self.dot_repr())
#
# def request_svg(self): # pragma: no cover
# from tempfile import NamedTemporaryFile
# import requests
# from ..common.compat import ensure_binary
# response = requests.post("https://condaviz.glitch.me/post",
# data={"digraph": self.dot_repr()})
# response.raise_for_status()
# with NamedTemporaryFile(suffix='.svg', delete=False) as fh:
# fh.write(ensure_binary(response.text))
# print("saved to: %s" % fh.name, file=sys.stderr)
# return fh.name
#
# def open_url(self): # pragma: no cover
# import webbrowser
# from ..common.url import path_to_url
# location = self.request_svg()
# try:
# browser = webbrowser.get("safari")
# except webbrowser.Error:
# browser = webbrowser.get()
# browser.open_new_tab(path_to_url(location))
class GeneralGraph(PrefixGraph):
"""
Compared with PrefixGraph, this class takes in more than one record of a given name,
and operates on that graph from the higher view across any matching dependencies. It is
not a Prefix thing, but more like a "graph of all possible candidates" thing, and is used
for unsatisfiability analysis
"""
def __init__(self, records, specs=()):
records = tuple(records)
super().__init__(records, specs)
self.specs_by_name = defaultdict(dict)
for node in records:
parent_dict = self.specs_by_name.get(node.name, {})
for dep in tuple(MatchSpec(d) for d in node.depends):
deps = parent_dict.get(dep.name, set())
deps.add(dep)
parent_dict[dep.name] = deps
self.specs_by_name[node.name] = parent_dict
consolidated_graph = {}
# graph is toposorted, so looping over it is in dependency order
for node, parent_nodes in reversed(list(self.graph.items())):
cg = consolidated_graph.get(node.name, set())
cg.update(_.name for _ in parent_nodes)
consolidated_graph[node.name] = cg
self.graph_by_name = consolidated_graph
def breadth_first_search_by_name(self, root_spec, target_spec):
"""Return shorted path from root_spec to spec_name"""
queue = []
queue.append([root_spec])
visited = []
while queue:
path = queue.pop(0)
node = path[-1]
if node in visited:
continue
visited.append(node)
if node == target_spec:
return path
children = []
specs = self.specs_by_name.get(node.name)
if specs is None:
continue
for _, deps in specs.items():
children.extend(list(deps))
for adj in children:
if adj.name == target_spec.name and adj.version != target_spec.version:
pass
else:
new_path = list(path)
new_path.append(adj)
queue.append(new_path)