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

Test Failed

Pull Request — master (#58)

unknown

created 2020-07-01 23:51 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	0
CRAP Score	42

Importance

Changes

Metric	Value
cc	6
eloc	25
nop	5
dl	0
loc	30
rs	8.3466
c	0
b	0
f	0
ccs	0
cts	0
cp	0
crap	42

"""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_function = 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_function(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_functions = {}

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

    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_function(self.graph,
                                             source, destination, parameter))
        except (NodeNotFound, NetworkXNoPath):
            return []
        return paths

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

kytos / pathfinder

Pull Request — master (#58)

KytosGraph.constrained_flexible_paths() B

Complexity

Size

Duplication

Code Coverage

Importance

Duplication Side-by-Side

Filter issues like