build.graph.graph.KytosGraph.__init__() - Code Metrics - Inspection of "Feat/negative ownership" - kytos-ng/pathfinder - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — master (#65)

unknown

created 2023-12-13 14:37 UTC

build.graph.graph.KytosGraph.init() B

↳ Parent: build.graph.graph

Complexity

Conditions

Size

Total Lines	56
Code Lines	44

Duplication

Lines	0
Ratio	0 %

Code Coverage

Tests	6
CRAP Score	6

Importance

Changes

Metric	Value
cc	6
eloc	44
nop	1
dl	0
loc	56
ccs	6
cts	6
cp	1
crap	6
rs	7.8906
c	0
b	0
f	0

How to fix Long Method

"""Module Graph of kytos/pathfinder Kytos Network Application."""

# pylint: enable=too-many-arguments,too-many-locals
from itertools import combinations, islice
import operator

from kytos.core import log
from kytos.core.common import EntityStatus
 
from .filters import EdgeFilter, ProcessEdgeAttribute, TypeCheckPreprocessor, TypeDifferentiatedProcessor, UseDefaultIfNone, UseValIfNone
from .weights import (nx_edge_data_delay, nx_edge_data_priority, nx_edge_data_weight)


try:
    import networkx as nx
    from networkx.exception import NetworkXNoPath, NodeNotFound
except ImportError:
    PACKAGE = "networkx==2.5.1"
    log.error(f"Package {PACKAGE} not found. Please 'pip install {PACKAGE}'")


class KytosGraph:
    """Class responsible for the graph generation."""

    def __init__(self):
        self.graph = nx.Graph()
        self._accepted_metadata = {
            'ownership',
            'bandwidth',
            'reliability',
            'priority',
            'utilization',
            'delay',
        }
        ownership_processor = ProcessEdgeAttribute(
            'ownership',
            TypeDifferentiatedProcessor({
                str: lambda x: frozenset(x.split()),
                dict: lambda x: frozenset(x.keys()),
                type(None): None
            })
        )
        self._filter_functions = {
            "ownership": EdgeFilter(
                operator.contains,
                UseValIfNone(ownership_processor)
            ),
            "not_ownership": EdgeFilter(
                lambda a, b: not (a & b),
                UseDefaultIfNone(ownership_processor, frozenset()),
                TypeDifferentiatedProcessor({
                    str: lambda val: frozenset(val.split(',')),
                    list: lambda val: frozenset(val)
                })
            ),
            "bandwidth": EdgeFilter(
                operator.ge,
                'bandwidth'
            ),
            "reliability": EdgeFilter(
                operator.ge,
                'reliability'
            ),
            "priority": EdgeFilter(
                operator.le,
                'priority'
            ),
            "utilization": EdgeFilter(
                operator.le,
                'utilization'
            ),
            "delay": EdgeFilter(
                operator.le,
                'delay'
            ),
        }
        self.spf_edge_data_cbs = {
            "hop": nx_edge_data_weight,
            "delay": nx_edge_data_delay,
            "priority": nx_edge_data_priority,
        }

    def clear(self):
        """Remove all nodes and links registered."""
        self.graph.clear()

    def update_topology(self, topology):
        """Update all nodes and links inside the graph."""
        self.graph.clear()
        self.update_nodes(topology.switches)
        self.update_links(topology.links)

    def update_nodes(self, nodes):
        """Update all nodes inside the graph."""
        for node in nodes.values():
            try:
                if node.status != EntityStatus.UP:
                    continue
                self.graph.add_node(node.id)

                for interface in node.interfaces.values():
                    if interface.status == EntityStatus.UP:
                        self.graph.add_node(interface.id)
                        self.graph.add_edge(node.id, interface.id)

            except AttributeError as err:
                raise TypeError(
                    f"Error when updating nodes inside the graph: {str(err)}"
                )

    def update_links(self, links):
        """Update all links inside the graph."""
        for link in links.values():
            if link.status == EntityStatus.UP:
                self.graph.add_edge(link.endpoint_a.id, link.endpoint_b.id)
                self.update_link_metadata(link)

    def update_link_metadata(self, link):
        """Update link metadata."""
        for key, value in link.metadata.items():
            if key not in self._accepted_metadata:
                continue
            endpoint_a = link.endpoint_a.id
            endpoint_b = link.endpoint_b.id
            self.graph[endpoint_a][endpoint_b][key] = value

    def get_link_metadata(self, endpoint_a, endpoint_b):
        """Return the metadata of a link."""
        return self.graph.get_edge_data(endpoint_a, endpoint_b)

    @staticmethod
    def _remove_switch_hops(circuit):
        """Remove switch hops from a circuit hops list."""
        for hop in circuit["hops"]:
            if len(hop.split(":")) == 8:
                circuit["hops"].remove(hop)

    def _path_cost(self, path, weight="hop", default_cost=1):
        """Compute the path cost given an attribute."""
        cost = 0
        for node, nbr in nx.utils.pairwise(path):
            cost += self.graph[node][nbr].get(weight, default_cost)
        return cost

    def path_cost_builder(self, paths, weight="hop", default_weight=1):
        """Build the cost of a path given a list of paths."""
        paths_acc = []
        for path in paths:
            if isinstance(path, list):
                paths_acc.append(
                    {
                        "hops": path,
                        "cost": self._path_cost(
                            path, weight=weight, default_cost=default_weight
                        ),
                    }
                )
            elif isinstance(path, dict):
                path["cost"] = self._path_cost(
                    path["hops"], weight=weight, default_cost=default_weight
                )
                paths_acc.append(path)
            else:
                raise TypeError(
                    f"type: '{type(path)}' must be be either list or dict. "
                    f"path: {path}"
                )
        return paths_acc

    def k_shortest_paths(
        self, source, destination, weight=None, k=1, graph=None
    ):
        """
        Compute up to k shortest paths and return them.

        This procedure is based on algorithm by Jin Y. Yen [1].
        Since Yen's algorithm calls Dijkstra's up to k times, the time
        complexity will be proportional to K * Dijkstra's, average
        O(K(|V| + |E|)logV), assuming it's using a heap, where V is the
        number of vertices and E number of egdes.

        References
        ----------
        .. [1] Jin Y. Yen, "Finding the K Shortest Loopless Paths in a
           Network", Management Science, Vol. 17, No. 11, Theory Series
           (Jul., 1971), pp. 712-716.
        """
        try:
            return list(
                islice(
                    nx.shortest_simple_paths(
                        graph or self.graph,
                        source,
                        destination,
                        weight=weight,
                    ),
                    k,
                )
            )
        except (NodeNotFound, NetworkXNoPath):
            return []

    def constrained_k_shortest_paths(
        self,
        source,
        destination,
        weight=None,
        k=1,
        graph=None,
        minimum_hits=None,
        **metrics,
    ):
        """Calculate the constrained shortest paths with flexibility."""
        graph = graph or self.graph
        mandatory_metrics = metrics.get("mandatory_metrics", {})
        flexible_metrics = metrics.get("flexible_metrics", {})
        first_pass_links = list(
            self._filter_links(
                graph.edges(data=True), **mandatory_metrics
            )
        )
        length = len(flexible_metrics)
        if minimum_hits is None:
            minimum_hits = 0
        minimum_hits = min(length, max(0, minimum_hits))

        paths = []
        for i in range(length, minimum_hits - 1, -1):
            for combo in combinations(flexible_metrics.items(), i):
                additional = dict(combo)
                filtered_links = self._filter_links(
                    first_pass_links, **additional
                )
                filtered_links = ((u, v) for u, v, d in filtered_links)

                for path in self.k_shortest_paths(
                    source,
                    destination,
                    weight=weight,
                    k=k,
                    graph=graph.edge_subgraph(filtered_links),
                ):
                    paths.append(
                        {
                            "hops": path,
                            "metrics": {**mandatory_metrics, **additional},
                        }
                    )
                if len(paths) == k:
                    return paths
            if paths:
                return paths
        return paths

    def _filter_links(self, links, **metrics):
        for metric, value in metrics.items():
            filter_func = self._filter_functions.get(metric, None)
            if filter_func is not None:
                try:
                    links = filter_func(value, links)
                except TypeError as err:
                    raise TypeError(
                        f"Error in {metric} value: {value} err: {err}"
                    )
        return links


1		"""Module Graph of kytos/pathfinder Kytos Network Application."""
2
3		# pylint: enable=too-many-arguments,too-many-locals
4	1	from itertools import combinations, islice
5	1	import operator
6
7	1	from kytos.core import log
8	1	from kytos.core.common import EntityStatus
9
10	1	from .filters import EdgeFilter, ProcessEdgeAttribute, TypeCheckPreprocessor, TypeDifferentiatedProcessor, UseDefaultIfNone, UseValIfNone
11	1	from .weights import (nx_edge_data_delay, nx_edge_data_priority, nx_edge_data_weight)
12
13
14	1	try:
15	1	import networkx as nx
16	1	from networkx.exception import NetworkXNoPath, NodeNotFound
17		except ImportError:
18		PACKAGE = "networkx==2.5.1"
19		log.error(f"Package {PACKAGE} not found. Please 'pip install {PACKAGE}'")
20
21
22	1	class KytosGraph:
23		"""Class responsible for the graph generation."""
24
25	1	def __init__(self):
26	1	self.graph = nx.Graph()
27	1	self._accepted_metadata = {
28		'ownership',
29		'bandwidth',
30		'reliability',
31		'priority',
32		'utilization',
33		'delay',
34		}
35	1	ownership_processor = ProcessEdgeAttribute(
36		'ownership',
37		TypeDifferentiatedProcessor({
38		str: lambda x: frozenset(x.split()),
39		dict: lambda x: frozenset(x.keys()),
40		type(None): None
41		})
42		)
43	1	self._filter_functions = {
44		"ownership": EdgeFilter(
45		operator.contains,
46		UseValIfNone(ownership_processor)
47		),
48		"not_ownership": EdgeFilter(
49		lambda a, b: not (a & b),
50		UseDefaultIfNone(ownership_processor, frozenset()),
51		TypeDifferentiatedProcessor({
52		str: lambda val: frozenset(val.split(',')),
53		list: lambda val: frozenset(val)
54		})
55		),
56		"bandwidth": EdgeFilter(
57		operator.ge,
58		'bandwidth'
59		),
60		"reliability": EdgeFilter(
61		operator.ge,
62		'reliability'
63		),
64		"priority": EdgeFilter(
65		operator.le,
66		'priority'
67		),
68		"utilization": EdgeFilter(
69		operator.le,
70		'utilization'
71		),
72		"delay": EdgeFilter(
73		operator.le,
74		'delay'
75		),
76		}
77	1	self.spf_edge_data_cbs = {
78		"hop": nx_edge_data_weight,
79		"delay": nx_edge_data_delay,
80		"priority": nx_edge_data_priority,
81		}
82
83	1	def clear(self):
84		"""Remove all nodes and links registered."""
85	1	self.graph.clear()
86
87	1	def update_topology(self, topology):
88		"""Update all nodes and links inside the graph."""
89	1	self.graph.clear()
90	1	self.update_nodes(topology.switches)
91	1	self.update_links(topology.links)
92
93	1	def update_nodes(self, nodes):
94		"""Update all nodes inside the graph."""
95	1	for node in nodes.values():
96	1	try:
97	1	if node.status != EntityStatus.UP:
98	1	continue
99	1	self.graph.add_node(node.id)
100
101	1	for interface in node.interfaces.values():
102	1	if interface.status == EntityStatus.UP:
103	1	self.graph.add_node(interface.id)
104	1	self.graph.add_edge(node.id, interface.id)
105
106	1	except AttributeError as err:
107	1	raise TypeError(
108		f"Error when updating nodes inside the graph: {str(err)}"
109		)
110
111	1	def update_links(self, links):
112		"""Update all links inside the graph."""
113	1	for link in links.values():
114	1	if link.status == EntityStatus.UP:
115	1	self.graph.add_edge(link.endpoint_a.id, link.endpoint_b.id)
116	1	self.update_link_metadata(link)
117
118	1	def update_link_metadata(self, link):
119		"""Update link metadata."""
120	1	for key, value in link.metadata.items():
121	1	if key not in self._accepted_metadata:
122	1	continue
123	1	endpoint_a = link.endpoint_a.id
124	1	endpoint_b = link.endpoint_b.id
125	1	self.graph[endpoint_a][endpoint_b][key] = value
126
127	1	def get_link_metadata(self, endpoint_a, endpoint_b):
128		"""Return the metadata of a link."""
129	1	return self.graph.get_edge_data(endpoint_a, endpoint_b)
130
131	1	@staticmethod
132	1	def _remove_switch_hops(circuit):
133		"""Remove switch hops from a circuit hops list."""
134	1	for hop in circuit["hops"]:
135	1	if len(hop.split(":")) == 8:
136	1	circuit["hops"].remove(hop)
137
138	1	def _path_cost(self, path, weight="hop", default_cost=1):
139		"""Compute the path cost given an attribute."""
140	1	cost = 0
141	1	for node, nbr in nx.utils.pairwise(path):
142	1	cost += self.graph[node][nbr].get(weight, default_cost)
143	1	return cost
144
145	1	def path_cost_builder(self, paths, weight="hop", default_weight=1):
146		"""Build the cost of a path given a list of paths."""
147	1	paths_acc = []
148	1	for path in paths:
149	1	if isinstance(path, list):
150	1	paths_acc.append(
151		{
152		"hops": path,
153		"cost": self._path_cost(
154		path, weight=weight, default_cost=default_weight
155		),
156		}
157		)
158	1	elif isinstance(path, dict):
159	1	path["cost"] = self._path_cost(
160		path["hops"], weight=weight, default_cost=default_weight
161		)
162	1	paths_acc.append(path)
163		else:
164		raise TypeError(
165		f"type: '{type(path)}' must be be either list or dict. "
166		f"path: {path}"
167		)
168	1	return paths_acc
169
170	1	def k_shortest_paths(
171		self, source, destination, weight=None, k=1, graph=None
172		):
173		"""
174		Compute up to k shortest paths and return them.
175
176		This procedure is based on algorithm by Jin Y. Yen [1].
177		Since Yen's algorithm calls Dijkstra's up to k times, the time
178		complexity will be proportional to K * Dijkstra's, average
179		O(K(\|V\| + \|E\|)logV), assuming it's using a heap, where V is the
180		number of vertices and E number of egdes.
181
182		References
183		----------
184		.. [1] Jin Y. Yen, "Finding the K Shortest Loopless Paths in a
185		Network", Management Science, Vol. 17, No. 11, Theory Series
186		(Jul., 1971), pp. 712-716.
187		"""
188	1	try:
189	1	return list(
190		islice(
191		nx.shortest_simple_paths(
192		graph or self.graph,
193		source,
194		destination,
195		weight=weight,
196		),
197		k,
198		)
199		)
200	1	except (NodeNotFound, NetworkXNoPath):
201	1	return []
202
203	1	def constrained_k_shortest_paths(
204		self,
205		source,
206		destination,
207		weight=None,
208		k=1,
209		graph=None,
210		minimum_hits=None,
211		**metrics,
212		):
213		"""Calculate the constrained shortest paths with flexibility."""
214	1	graph = graph or self.graph
215	1	mandatory_metrics = metrics.get("mandatory_metrics", {})
216	1	flexible_metrics = metrics.get("flexible_metrics", {})
217	1	first_pass_links = list(
218		self._filter_links(
219		graph.edges(data=True), **mandatory_metrics
220		)
221		)
222	1	length = len(flexible_metrics)
223	1	if minimum_hits is None:
224	1	minimum_hits = 0
225	1	minimum_hits = min(length, max(0, minimum_hits))
226
227	1	paths = []
228	1	for i in range(length, minimum_hits - 1, -1):
229	1	for combo in combinations(flexible_metrics.items(), i):
230	1	additional = dict(combo)
231	1	filtered_links = self._filter_links(
232		first_pass_links, **additional
233		)
234	1	filtered_links = ((u, v) for u, v, d in filtered_links)
		0 ignored issues – show introduced 2021-11-30 20:18 UTC by Report Bug Copy Issue Report The variable `u` does not seem to be defined for all execution paths. Loading history... introduced 2021-11-30 20:18 UTC by Report Bug Copy Issue Report The variable `v` does not seem to be defined for all execution paths. Loading history...
235	1	for path in self.k_shortest_paths(
236		source,
237		destination,
238		weight=weight,
239		k=k,
240		graph=graph.edge_subgraph(filtered_links),
241		):
242	1	paths.append(
243		{
244		"hops": path,
245		"metrics": {mandatory_metrics, additional},
246		}
247		)
248	1	if len(paths) == k:
249	1	return paths
250	1	if paths:
251	1	return paths
252	1	return paths
253
254	1	def _filter_links(self, links, **metrics):
255	1	for metric, value in metrics.items():
256	1	filter_func = self._filter_functions.get(metric, None)
257	1	if filter_func is not None:
258	1	try:
259	1	links = filter_func(value, links)
260		except TypeError as err:
261		raise TypeError(
262		f"Error in {metric} value: {value} err: {err}"
263		)
264		return links
265

kytos-ng / pathfinder

Pull Request — master (#65)

build.graph.graph.KytosGraph.__init__() B

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build.graph.graph.KytosGraph.init() B