build.graph.KytosGraph._constrained_shortest_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 2020-06-17 20:42 UTC

KytosGraph._constrained_shortest_paths() A

↳ Parent: build.graph

Complexity

Conditions

Size

Total Lines	12
Code Lines	12

Duplication

Lines	0
Ratio	0 %

Code Coverage

Tests	0
CRAP Score	30

Importance

Changes

Metric	Value
cc	5
eloc	12
nop	4
dl	0
loc	12
rs	9.3333
c	0
b	0
f	0
ccs	0
cts	0
cp	0
crap	30

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


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


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


        self._filter_fun_dict["ownership"] = Filter(
            str, filterEEQ("ownership"))
        self._filter_fun_dict["bandwidth"] = Filter(
            (int, float), filterGEQ("bandwidth"))
        self._filter_fun_dict["priority"] = Filter(
            (int, float), filterGEQ("priority"))
        self._filter_fun_dict["reliability"] = Filter(
            (int, float), filterGEQ("reliability"))
        self._filter_fun_dict["utilization"] = Filter(
            (int, float), filterLEQ("utilization"))
        self._filter_fun_dict["delay"] = Filter(
            (int, float), filterLEQ("delay"))
        self._path_fun = nx.all_shortest_paths

    def set_path_fun(self, path_fun):
        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, metrics,
                                   flexible_metrics, flexible=None):
        '''Calculate the constrained shortest paths with flexibility.'''
        default_edge_list = list(self._filter_edges(
            self.graph.edges(data=True), **metrics))
        length = len(flexible_metrics)
        if flexible is None:
            flexible = length
        flexible = min(length, max(0, flexible))
        results = []
        stop = False
        i = 0

        while (not stop and i in range(0, flexible+1)):
            combos = combinations(flexible_metrics.items(), length-i)
            for combo in combos:
                temp_dict = {}
                for metric, value in combo:
                    temp_dict[metric] = value
                edges = self._filter_edges(default_edge_list, **temp_dict)
                edges = ((u, v) for u, v, d in edges)

                result = self._constrained_shortest_paths(
                    source, destination, edges)
                if result != []:
                    results.append(
                        {"paths": result, "metrics": {**metrics, **temp_dict}})
                    stop = True
            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	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_fun = 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_fun(value), items)
26	1	else:
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_fun_dict = {}
36	1
37	1	def filterLEQ(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 filterGEQ(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 filterEEQ(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_fun_dict["ownership"] = Filter(
47	1	str, filterEEQ("ownership"))
48	1	self._filter_fun_dict["bandwidth"] = Filter(
49	1	(int, float), filterGEQ("bandwidth"))
50	1	self._filter_fun_dict["priority"] = Filter(
51	1	(int, float), filterGEQ("priority"))
52	1	self._filter_fun_dict["reliability"] = Filter(
53		(int, float), filterGEQ("reliability"))
54	1	self._filter_fun_dict["utilization"] = Filter(
55		(int, float), filterLEQ("utilization"))
56	1	self._filter_fun_dict["delay"] = Filter(
57		(int, float), filterLEQ("delay"))
58		self._path_fun = nx.all_shortest_paths
59
60		def set_path_fun(self, path_fun):
61		self._path_fun = path_fun
62	1
63	1	def clear(self):
64	1	"""Remove all nodes and links registered."""
65	1	self.graph.clear()
66
67	1	def update_topology(self, topology):
68		"""Update all nodes and links inside the graph."""
69		self.graph.clear()
70	1	self.update_nodes(topology.switches)
71	1	self.update_links(topology.links)
72	1
73		def update_nodes(self, nodes):
74	1	"""Update all nodes inside the graph."""
75		for node in nodes.values():
76	1	try:
77	1	self.graph.add_node(node.id)
78
79		for interface in node.interfaces.values():
80		self.graph.add_node(interface.id)
81		self.graph.add_edge(node.id, interface.id)
82	1
83		except AttributeError:
84		pass
85
86		def update_links(self, links):
87		"""Update all links inside the graph."""
88		keys = []
89		for link in links.values():
90		if link.is_active():
91		self.graph.add_edge(link.endpoint_a.id, link.endpoint_b.id)
92		for key, value in link.metadata.items():
93		keys.append(key)
94		endpoint_a = link.endpoint_a.id
95		endpoint_b = link.endpoint_b.id
96		self.graph[endpoint_a][endpoint_b][key] = value
97
98		def get_metadata_from_link(self, endpoint_a, endpoint_b):
99		"""Return the metadata of a link."""
100		return self.graph.edges[endpoint_a, endpoint_b]
101
102		@staticmethod
103		def _remove_switch_hops(circuit):
104		"""Remove switch hops from a circuit hops list."""
105		for hop in circuit['hops']:
106		if len(hop.split(':')) == 8:
107		circuit['hops'].remove(hop)
108
109		def shortest_paths(self, source, destination, parameter=None):
110		"""Calculate the shortest paths and return them."""
111		try:
112		paths = list(self._path_fun(self.graph,
113		source, destination, parameter))
114		except (NodeNotFound, NetworkXNoPath):
115		return []
116		return paths
117
118		def constrained_flexible_paths(self, source, destination, metrics,
119		flexible_metrics, flexible=None):
120		'''Calculate the constrained shortest paths with flexibility.'''
121		default_edge_list = list(self._filter_edges(
122		self.graph.edges(data=True), **metrics))
123		length = len(flexible_metrics)
124		if flexible is None:
125		flexible = length
126		flexible = min(length, max(0, flexible))
127		results = []
128		stop = False
129		i = 0
130
131		while (not stop and i in range(0, flexible+1)):
132		combos = combinations(flexible_metrics.items(), length-i)
133		for combo in combos:
134		temp_dict = {}
135		for metric, value in combo:
136		temp_dict[metric] = value
137		edges = self._filter_edges(default_edge_list, **temp_dict)
138		edges = ((u, v) for u, v, d in edges)
		0 ignored issues – show introduced 2020-06-16 17:15 UTC by Report Bug Copy Issue Report The variable `v` does not seem to be defined for all execution paths. Loading history... introduced 2020-05-28 14:34 UTC by Report Bug Copy Issue Report The variable `u` does not seem to be defined for all execution paths. Loading history...
139		result = self._constrained_shortest_paths(
140		source, destination, edges)
141		if result != []:
142		results.append(
143		{"paths": result, "metrics": {metrics, temp_dict}})
144		stop = True
145		i = i + 1
146		return results
147
148		def _constrained_shortest_paths(self, source, destination, edges):
149		paths = []
150		try:
151		paths = list(self._path_fun(self.graph.edge_subgraph(edges),
152		source, destination))
153		except NetworkXNoPath:
154		pass
155		except NodeNotFound:
156		if source == destination:
157		if source in self.graph.nodes:
158		paths = [[source]]
159		return paths
160
161		def _filter_edges(self, edges, **metrics):
162		for metric, value in metrics.items():
163		fil = self._filter_fun_dict.get(metric, None)
164		if fil is not None:
165		try:
166		edges = fil.run(value, edges)
167		except TypeError as err:
168		raise TypeError(f"Error in {metric} filter: {err}")
169		return edges
170

kytos / pathfinder

Pull Request — master (#58)

KytosGraph._constrained_shortest_paths() A

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