build.graph.KytosGraph.constrained_flexible_paths() - Code Metrics - Inspection of "Logic Changes" - kytos/pathfinder - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Pull Request — master (#58)

unknown

created 2020-06-25 00:14 UTC

KytosGraph.constrained_flexible_paths() B

↳ Parent: build.graph

Complexity

Conditions

Size

Total Lines	30
Code Lines	25

Duplication

Lines	0
Ratio	0 %

Code Coverage

Tests	19
CRAP Score	6

Importance

Changes

Metric	Value
cc	6
eloc	25
nop	5
dl	0
loc	30
rs	8.3466
c	0
b	0
f	0
ccs	19
cts	19
cp	1
crap	6

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

from itertools import combinations

from kytos.core import log

try:
    import networkx as nx
    from networkx.exception import NodeNotFound, NetworkXNoPath
except ImportError:
    PACKAGE = 'networkx>=2.2'
    log.error(f"Package {PACKAGE} not found. Please 'pip install {PACKAGE}'")


class Filter:
    """Class responsible for removing items with disqualifying values."""

    def __init__(self, filter_type, filter_function):
        self._filter_type = filter_type
        self._filter_fun = filter_function

    def run(self, value, items):
        """Filter out items. Filter chosen is picked at runtime."""
        if isinstance(value, self._filter_type):
            return filter(self._filter_fun(value), items)

        raise TypeError(f"Expected type: {self._filter_type}")


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

    def __init__(self):
        self.graph = nx.Graph()
        self._filter_fun_dict = {}

        def filter_leq(metric):  # Lower values are better
            return lambda x: (lambda y: y[2].get(metric, x) <= x)


        def filter_geq(metric):  # Higher values are better
            return lambda x: (lambda y: y[2].get(metric, x) >= x)


        def filter_eeq(metric):  # Equivalence
            return lambda x: (lambda y: y[2].get(metric, x) == x)


        self._filter_fun_dict["ownership"] = Filter(
            str, filter_eeq("ownership"))
        self._filter_fun_dict["bandwidth"] = Filter(
            (int, float), filter_geq("bandwidth"))
        self._filter_fun_dict["priority"] = Filter(
            (int, float), filter_geq("priority"))
        self._filter_fun_dict["reliability"] = Filter(
            (int, float), filter_geq("reliability"))
        self._filter_fun_dict["utilization"] = Filter(
            (int, float), filter_leq("utilization"))
        self._filter_fun_dict["delay"] = Filter(
            (int, float), filter_leq("delay"))
        self._path_fun = nx.all_shortest_paths

    def set_path_fun(self, path_fun):
        """Set the shortest path function."""
        self._path_fun = path_fun

    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:
                self.graph.add_node(node.id)

                for interface in node.interfaces.values():
                    self.graph.add_node(interface.id)
                    self.graph.add_edge(node.id, interface.id)

            except AttributeError:
                pass

    def update_links(self, links):
        """Update all links inside the graph."""
        keys = []
        for link in links.values():
            if link.is_active():
                self.graph.add_edge(link.endpoint_a.id, link.endpoint_b.id)
                for key, value in link.metadata.items():
                    keys.append(key)
                    endpoint_a = link.endpoint_a.id
                    endpoint_b = link.endpoint_b.id
                    self.graph[endpoint_a][endpoint_b][key] = value

    def get_metadata_from_link(self, endpoint_a, endpoint_b):
        """Return the metadata of a link."""
        return self.graph.edges[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 shortest_paths(self, source, destination, parameter=None):
        """Calculate the shortest paths and return them."""
        try:
            paths = list(self._path_fun(self.graph,
                                        source, destination, parameter))
        except (NodeNotFound, NetworkXNoPath):
            return []
        return paths

    def constrained_flexible_paths(self, source, destination,
                                   depth=None, **metrics):
        """Calculate the constrained shortest paths with flexibility."""
        base = metrics.get("base", {})
        flexible = metrics.get("flexible", {})
        # Retrive subgraph with edges that meet base requirements.
        default_edge_list = list(self._filter_edges(
            self.graph.edges(data=True), **base))
        length = len(flexible)
        if depth is None:
            depth = length
        depth = min(length, max(0, depth))
        results = []
        paths = []
        i = 0
        # Create "sub-subgraphs" from original subgraph by trimming edges
        # that fail to meet flexible requirements. Search for a shortest
        # path in each of these graphs, until at least one is found.
        while (paths == [] and i in range(0, depth+1)):
            for combo in combinations(flexible.items(), length-i):
                additional = dict(combo)
                paths = self._constrained_shortest_paths(
                    source, destination, ((u, v) for u, v, d in

                                          self._filter_edges(default_edge_list,
                                                             **additional)))
                if paths != []:
                    results.append(
                        {"paths": paths, "metrics": {**base, **additional}})
            i = i + 1
        return results

    def _constrained_shortest_paths(self, source, destination, edges):
        paths = []
        try:
            paths = list(self._path_fun(self.graph.edge_subgraph(edges),
                                        source, destination))
        except NetworkXNoPath:
            pass
        except NodeNotFound:
            if source == destination:
                if source in self.graph.nodes:
                    paths = [[source]]
        return paths

    def _filter_edges(self, edges, **metrics):
        for metric, value in metrics.items():
            fil = self._filter_fun_dict.get(metric, None)
            if fil is not None:
                try:
                    edges = fil.run(value, edges)
                except TypeError as err:
                    raise TypeError(f"Error in {metric} filter: {err}")
        return edges


1		"""Module Graph of kytos/pathfinder Kytos Network Application."""
2
3	1	from itertools import combinations
4
5	1	from kytos.core import log
6
7	1	try:
8	1	import networkx as nx
9	1	from networkx.exception import NodeNotFound, NetworkXNoPath
10		except ImportError:
11		PACKAGE = 'networkx>=2.2'
12		log.error(f"Package {PACKAGE} not found. Please 'pip install {PACKAGE}'")
13
14
15	1	class Filter:
16		"""Class responsible for removing items with disqualifying values."""
17
18	1	def __init__(self, filter_type, filter_function):
19	1	self._filter_type = filter_type
20	1	self._filter_fun = filter_function
21
22	1	def run(self, value, items):
23		"""Filter out items. Filter chosen is picked at runtime."""
24	1	if isinstance(value, self._filter_type):
25	1	return filter(self._filter_fun(value), items)
26
27	1	raise TypeError(f"Expected type: {self._filter_type}")
28
29
30	1	class KytosGraph:
31		"""Class responsible for the graph generation."""
32
33	1	def __init__(self):
34	1	self.graph = nx.Graph()
35	1	self._filter_fun_dict = {}
36
37	1	def filter_leq(metric): # Lower values are better
38	1	return lambda x: (lambda y: y[2].get(metric, x) <= x)
		0 ignored issues – show Comprehensibility Best Practice introduced 2020-05-28 14:34 UTC by Report Bug Copy Issue Report The variable `x` does not seem to be defined. Loading history...
39
40	1	def filter_geq(metric): # Higher values are better
41	1	return lambda x: (lambda y: y[2].get(metric, x) >= x)
		0 ignored issues – show Comprehensibility Best Practice introduced 2020-05-28 14:34 UTC by Report Bug Copy Issue Report The variable `x` does not seem to be defined. Loading history...
42
43	1	def filter_eeq(metric): # Equivalence
44	1	return lambda x: (lambda y: y[2].get(metric, x) == x)
		0 ignored issues – show Comprehensibility Best Practice introduced 2020-05-28 14:34 UTC by Report Bug Copy Issue Report The variable `x` does not seem to be defined. Loading history...
45
46	1	self._filter_fun_dict["ownership"] = Filter(
47		str, filter_eeq("ownership"))
48	1	self._filter_fun_dict["bandwidth"] = Filter(
49		(int, float), filter_geq("bandwidth"))
50	1	self._filter_fun_dict["priority"] = Filter(
51		(int, float), filter_geq("priority"))
52	1	self._filter_fun_dict["reliability"] = Filter(
53		(int, float), filter_geq("reliability"))
54	1	self._filter_fun_dict["utilization"] = Filter(
55		(int, float), filter_leq("utilization"))
56	1	self._filter_fun_dict["delay"] = Filter(
57		(int, float), filter_leq("delay"))
58	1	self._path_fun = nx.all_shortest_paths
59
60	1	def set_path_fun(self, path_fun):
61		"""Set the shortest path function."""
62	1	self._path_fun = path_fun
63
64	1	def clear(self):
65		"""Remove all nodes and links registered."""
66	1	self.graph.clear()
67
68	1	def update_topology(self, topology):
69		"""Update all nodes and links inside the graph."""
70	1	self.graph.clear()
71	1	self.update_nodes(topology.switches)
72	1	self.update_links(topology.links)
73
74	1	def update_nodes(self, nodes):
75		"""Update all nodes inside the graph."""
76	1	for node in nodes.values():
77	1	try:
78	1	self.graph.add_node(node.id)
79
80	1	for interface in node.interfaces.values():
81	1	self.graph.add_node(interface.id)
82	1	self.graph.add_edge(node.id, interface.id)
83
84		except AttributeError:
85		pass
86
87	1	def update_links(self, links):
88		"""Update all links inside the graph."""
89	1	keys = []
90	1	for link in links.values():
91	1	if link.is_active():
92	1	self.graph.add_edge(link.endpoint_a.id, link.endpoint_b.id)
93	1	for key, value in link.metadata.items():
94	1	keys.append(key)
95	1	endpoint_a = link.endpoint_a.id
96	1	endpoint_b = link.endpoint_b.id
97	1	self.graph[endpoint_a][endpoint_b][key] = value
98
99	1	def get_metadata_from_link(self, endpoint_a, endpoint_b):
100		"""Return the metadata of a link."""
101	1	return self.graph.edges[endpoint_a, endpoint_b]
102
103	1	@staticmethod
104		def _remove_switch_hops(circuit):
105		"""Remove switch hops from a circuit hops list."""
106	1	for hop in circuit['hops']:
107	1	if len(hop.split(':')) == 8:
108	1	circuit['hops'].remove(hop)
109
110	1	def shortest_paths(self, source, destination, parameter=None):
111		"""Calculate the shortest paths and return them."""
112	1	try:
113	1	paths = list(self._path_fun(self.graph,
114		source, destination, parameter))
115	1	except (NodeNotFound, NetworkXNoPath):
116	1	return []
117	1	return paths
118
119	1	def constrained_flexible_paths(self, source, destination,
120		depth=None, **metrics):
121		"""Calculate the constrained shortest paths with flexibility."""
122	1	base = metrics.get("base", {})
123	1	flexible = metrics.get("flexible", {})
124		# Retrive subgraph with edges that meet base requirements.
125	1	default_edge_list = list(self._filter_edges(
126		self.graph.edges(data=True), **base))
127	1	length = len(flexible)
128	1	if depth is None:
129	1	depth = length
130	1	depth = min(length, max(0, depth))
131	1	results = []
132	1	paths = []
133	1	i = 0
134		# Create "sub-subgraphs" from original subgraph by trimming edges
135		# that fail to meet flexible requirements. Search for a shortest
136		# path in each of these graphs, until at least one is found.
137	1	while (paths == [] and i in range(0, depth+1)):
138	1	for combo in combinations(flexible.items(), length-i):
139	1	additional = dict(combo)
140	1	paths = self._constrained_shortest_paths(
141		source, destination, ((u, v) for u, v, d in
		0 ignored issues – show introduced 2020-06-18 21:53 UTC by Report Bug Copy Issue Report The variable `v` does not seem to be defined for all execution paths. Loading history... introduced 2020-06-18 21:53 UTC by Report Bug Copy Issue Report The variable `u` does not seem to be defined for all execution paths. Loading history...
142		self._filter_edges(default_edge_list,
143		**additional)))
144	1	if paths != []:
145	1	results.append(
146		{"paths": paths, "metrics": {base, additional}})
147	1	i = i + 1
148	1	return results
149
150	1	def _constrained_shortest_paths(self, source, destination, edges):
151	1	paths = []
152	1	try:
153	1	paths = list(self._path_fun(self.graph.edge_subgraph(edges),
154		source, destination))
155	1	except NetworkXNoPath:
156	1	pass
157	1	except NodeNotFound:
158	1	if source == destination:
159	1	if source in self.graph.nodes:
160	1	paths = [[source]]
161	1	return paths
162
163	1	def _filter_edges(self, edges, **metrics):
164	1	for metric, value in metrics.items():
165	1	fil = self._filter_fun_dict.get(metric, None)
166	1	if fil is not None:
167	1	try:
168	1	edges = fil.run(value, edges)
169	1	except TypeError as err:
170	1	raise TypeError(f"Error in {metric} filter: {err}")
171		return edges
172

kytos / pathfinder

Pull Request — master (#58)

KytosGraph.constrained_flexible_paths() B

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