class ByteInterval(Node):
"""A contiguous region of bytes in a binary.
A ByteInterval defines a relative ordering for a group of
:class:`ByteBlock`\\s, optionally at a fixed address in memory. It also
stores the bytes associated with these blocks.
If two blocks are in two different ByteIntervals, then it should be
considered safe (that is, preserving of program semantics) to move one
block relative to the other in memory. If two blocks are in the same
ByteInterval, then it should be considered unknown if moving the two blocks
relative to one another in memory is a safe operation.
:ivar ~.address: The fixed address of this interval, if present. If this
field is present, it may indicate the original address at which this
interval was located at in memory, or it may indicate that this block's
address is fixed and must not be changed. If this field is not present,
it indicates that the interval is free to be moved around in memory
while preserving program semantics.
:ivar ~.size: The size of this interval in bytes. If this number is greater
than ``initialized_size``, this indicates that the high addresses taken
up by this interval consist of uninitialized bytes. This often occurs
in BSS sections, where data is zero-initialized rather than stored as
zeroes in the binary.
:ivar ~.contents: The bytes stored in this interval.
:ivar ~.blocks: A set of all :class:`ByteBlock`\\s in this interval.
:ivar ~.symbolic_expressions: A mapping, from offset in the interval, to a
:class:`SymbolicExpression` in the interval.
"""
class _BlockSet(SetWrapper[ByteBlock]):
def __init__(
self, node: "ByteInterval", *args: typing.Iterable[ByteBlock]
):
super().__init__()
self._node = node
self.update(*args)
def add(self, v: ByteBlock) -> None:
# We're defining add in terms of update so that we can optimize
# adding multiple blocks at once.
self.update((v,))
def update(self, *iterables: typing.Iterable[ByteBlock]) -> None:
node_ir = self._node.ir
new_items = set(*iterables) - self._data
for v in new_items:
if v._byte_interval is not None:
v._byte_interval.blocks.discard(v)
v._byte_interval = self._node
if node_ir is not None:
v._add_to_uuid_cache(node_ir._local_uuid_cache)
self._node._index_add_multiple(self._data, new_items)
self._data.update(new_items)
def discard(self, v: ByteBlock) -> None:
if v not in self:
return
self._node._index_discard(v)
v._byte_interval = None
if self._node.ir is not None:
v._remove_from_uuid_cache(self._node.ir._local_uuid_cache)
return super().discard(v)
class _SymbolicExprDict(DictWrapper[int, SymbolicExpression]):
def __init__(
self,
interval: "ByteInterval",
*args: DictLike[int, SymbolicExpression],
):
# Do not call the DictWrapper constructor: _SymbolicExprDict needs
# to store data in a SortedDict, not a regular dict.
self._interval = interval
self._data: "SortedDict[int, SymbolicExpression]" = SortedDict()
temp: typing.Dict[int, SymbolicExpression] = dict(*args)
for i, v in temp.items():
self[i] = v
def __repr__(self) -> str:
# We can't just return the repr of self._data because it will
# create a SortedDict and that isn't part of our public interface.
items = (
"{!r}: {!r}".format(key, value)
for key, value in self._data.items()
)
return "{" + ", ".join(items) + "}"
address = _IndexedAttribute[typing.Optional[int]]()(
lambda self: self.section
)
size = _IndexedAttribute[int]()(lambda self: self.section)
def __init__(
self,
*,
address: typing.Optional[int] = None,
size: typing.Optional[int] = None,
initialized_size: typing.Optional[int] = None,
contents: typing.ByteString = b"",
blocks: typing.Iterable[ByteBlock] = (),
symbolic_expressions: DictLike[int, SymbolicExpression] = {},
uuid: typing.Optional[UUID] = None,
section: typing.Optional["Section"] = None,
):
"""
:param address: The fixed address of this interval, if present.
:param size: The size of this interval in bytes.
:param initialized_size: The number of initialized bytes in this
interval.
:param contents: The bytes stored in this interval.
:param blocks: A set of all :class:`ByteBlock`\\s in this interval.
:param symbolic_expressions: A mapping, from offset in the interval, to
a :class:`SymbolicExpression` in the interval.
:param uuid: The UUID of this ``ByteInterval``,
or None if a new UUID needs generated via :func:`uuid.uuid4`.
Defaults to None.
:param section: The :class:`Section` this interval belongs to.
"""
if size is None:
size = len(contents)
if initialized_size is None:
initialized_size = len(contents)
if initialized_size > size:
raise ValueError("initialized_size must be <= size!")
super().__init__(uuid=uuid)
self._interval_tree: "IntervalTree[int, ByteBlock]" = IntervalTree()
self._section: typing.Optional["Section"] = None
self.address = address
self.size = size
self.contents = bytearray(contents)
self.initialized_size = initialized_size
self.blocks: SetWrapper[ByteBlock] = ByteInterval._BlockSet(
self, blocks
)
self._symbolic_expressions = ByteInterval._SymbolicExprDict(
self, symbolic_expressions
)
self._proto_interval: typing.Optional[
ByteInterval_pb2.ByteInterval
] = (None)
# Use the property setter to ensure correct invariants.
self.section = section
def _index_add_multiple(
self,
old_blocks: typing.Collection[ByteBlock],
new_blocks: typing.Collection[ByteBlock],
) -> None:
if len(old_blocks) < len(new_blocks):
self._interval_tree = IntervalTree(
_offset_interval(block)
for block in itertools.chain(old_blocks, new_blocks)
)
else:
for block in new_blocks:
self._index_add(block)
def _index_add(self, block: ByteBlock) -> None:
self._interval_tree.add(_offset_interval(block))
def _index_discard(self, block: ByteBlock) -> None:
self._interval_tree.discard(_offset_interval(block))
@property
def initialized_size(self) -> int:
"""The number of initialized bytes in this interval.
Not all bytes in this interval may correspond to bytes physically
stored in the underlying file format. This can occur, for example, in
BSS sections, which are zero-initialized at loadtime, but these zeroes
are not stored in the file itself. If this number is smaller than
``size``, this indicates that any bytes past this number are
unitialized bytes with values determined at loadtime. As such, all
bytes past this number in this interval's byte vector are truncated
when saving to file.
"""
return len(self.contents)
@initialized_size.setter
def initialized_size(self, value: int) -> None:
if value > len(self.contents):
self.contents += b"\0" * (value - len(self.contents))
elif value < len(self.contents):
self.contents = self.contents[:value]
@classmethod
def _decode_protobuf(
cls,
proto_interval: _NodeMessage,
uuid: UUID,
ir: typing.Optional["IR"],
) -> "ByteInterval":
assert ir
assert isinstance(proto_interval, ByteInterval_pb2.ByteInterval)
def decode_block(proto_block: ByteInterval_pb2.Block) -> ByteBlock:
block: ByteBlock
if proto_block.HasField("code"):
block = CodeBlock._from_protobuf(proto_block.code, ir)
elif proto_block.HasField("data"):
block = DataBlock._from_protobuf(proto_block.data, ir)
else:
raise TypeError(
"Unknown type inside proto block: %s"
% proto_block.WhichOneof("value")
)
block.offset = proto_block.offset
return block
# we do not decode symbolic expressions yet, because symbols have
# not yet been decoded at this point.
result = cls(
address=proto_interval.address
if proto_interval.has_address
else None,
size=proto_interval.size,
contents=proto_interval.contents,
uuid=uuid,
blocks=(decode_block(b) for b in proto_interval.blocks),
)
result._add_to_uuid_cache(ir._local_uuid_cache)
# We store the interval and IR here so we can use it later, when
# _decode_symbolic_expressions is called.
result._proto_interval = proto_interval
# Return the new BI.
return result
def _decode_symbolic_expressions(self, ir: "IR") -> None:
"""Called by modules after symbols are decoded, but before the module
is done decoding.
"""
assert self._proto_interval
def decode_symbolic_expression(
proto_expr: SymbolicExpression_pb2.SymbolicExpression,
) -> SymbolicExpression:
if proto_expr.HasField("addr_const"):
return SymAddrConst._from_protobuf(
proto_expr.addr_const, ir.get_by_uuid
)
elif proto_expr.HasField("addr_addr"):
return SymAddrAddr._from_protobuf(
proto_expr.addr_addr, ir.get_by_uuid
)
else:
raise TypeError(
"Unknown type inside proto sym expr: %s"
% proto_expr.WhichOneof("value")
)
self.symbolic_expressions = {}
for i, v in self._proto_interval.symbolic_expressions.items():
expr = decode_symbolic_expression(v)
expr.attributes = set(
SymbolicExpression.Attribute(f) for f in v.attribute_flags
)
self.symbolic_expressions[i] = expr
del self._proto_interval
def _to_protobuf(self) -> ByteInterval_pb2.ByteInterval:
proto_interval = ByteInterval_pb2.ByteInterval()
proto_interval.uuid = self.uuid.bytes
if self.address is None:
proto_interval.has_address = False
else:
proto_interval.has_address = True
proto_interval.address = self.address
proto_interval.size = self.size
proto_interval.contents = bytes(self.contents)
# Cannot insert blocks using proto_interval.blocks.append() in a loop
# because append() isn't supported in older versions of protobuf. Use a
# comprehension and extend() instead.
def to_proto_block(block: ByteBlock) -> ByteInterval_pb2.Block:
proto_block = ByteInterval_pb2.Block()
proto_block.offset = block.offset
if isinstance(block, CodeBlock):
proto_block.code.CopyFrom(block._to_protobuf())
elif isinstance(block, DataBlock):
proto_block.data.CopyFrom(block._to_protobuf())
else:
raise TypeError(
"Unknown block type in interval: %s" % type(block)
)
return proto_block
proto_interval.blocks.extend(to_proto_block(b) for b in self.blocks)
for k, v in self.symbolic_expressions.items():
sym_exp = SymbolicExpression_pb2.SymbolicExpression()
if isinstance(v, SymAddrConst):
sym_exp.addr_const.CopyFrom(v._to_protobuf())
elif isinstance(v, SymAddrAddr):
sym_exp.addr_addr.CopyFrom(v._to_protobuf())
else:
raise ValueError(
"Expected sym expr type in interval: %s" % type(v)
)
sym_exp.attribute_flags.extend(a.value for a in v.attributes)
proto_interval.symbolic_expressions[k].CopyFrom(sym_exp)
return proto_interval
@property
def section(self) -> typing.Optional["Section"]:
"""The :class:`Section` this interval belongs to."""
return self._section
@section.setter
def section(self, value: typing.Optional["Section"]) -> None:
if self._section is not None:
self._section.byte_intervals.discard(self)
if value is not None:
value.byte_intervals.add(self)
@property
def symbolic_expressions(
self,
) -> typing.MutableMapping[int, SymbolicExpression]:
return self._symbolic_expressions
@symbolic_expressions.setter
def symbolic_expressions(
self, value: typing.Dict[int, SymbolicExpression]
) -> None:
self._symbolic_expressions.clear()
self._symbolic_expressions.update(value)
def deep_eq(self, other: object) -> bool:
# Do not move __eq__. See docstring for Node.deep_eq for more info.
if not isinstance(other, ByteInterval):
return False
return (
self.uuid == other.uuid
and self.address == other.address
and self.contents == other.contents
and self.size == other.size
and len(self.blocks) == len(other.blocks)
and all(
self_node.deep_eq(other_node)
for self_node, other_node in zip(
sorted(self.blocks, key=lambda b: b.uuid),
sorted(other.blocks, key=lambda b: b.uuid),
)
)
and len(self.symbolic_expressions)
== len(other.symbolic_expressions)
and all(
self_kv[0] == other_kv[0] and self_kv[1].deep_eq(other_kv[1])
for self_kv, other_kv in zip(
sorted(
self.symbolic_expressions.items(), key=lambda kv: kv[0]
),
sorted(
other.symbolic_expressions.items(),
key=lambda kv: kv[0],
),
)
)
)
def __repr__(self) -> str:
return (
"ByteInterval("
"uuid={uuid!r}, "
"address={address}, "
"size={size}, "
"contents={contents!r}, "
"blocks={blocks!r}, "
"symbolic_expressions={symbolic_expressions!r}, "
")".format(
uuid=self.uuid,
address=self.address,
size=self.size,
contents=self.contents,
blocks=self.blocks,
symbolic_expressions=self.symbolic_expressions,
)
)
def byte_blocks_on(
self, addrs: typing.Union[int, range]
) -> typing.Iterable[ByteBlock]:
"""Finds all the byte blocks that overlap an address or range of
addresses.
:param addrs: Either a ``range`` object or a single address.
"""
if self.address is None:
return ()
return _nodes_on_interval_tree(
self._interval_tree, addrs, -self.address
)
def byte_blocks_at(
self, addrs: typing.Union[int, range]
) -> typing.Iterable[ByteBlock]:
"""Finds all the byte blocks that begin at an address or range of
addresses.
:param addrs: Either a ``range`` object or a single address.
"""
if self.address is None:
return ()
return _nodes_at_interval_tree(
self._interval_tree, addrs, -self.address
)
def code_blocks_on(
self, addrs: typing.Union[int, range]
) -> typing.Iterable[CodeBlock]:
"""Finds all the code blocks that overlap an address or range of
addresses.
:param addrs: Either a ``range`` object or a single address.
"""
return (
b for b in self.byte_blocks_on(addrs) if isinstance(b, CodeBlock)
)
def code_blocks_at(
self, addrs: typing.Union[int, range]
) -> typing.Iterable[CodeBlock]:
"""Finds all the code blocks that begin at an address or range of
addresses.
:param addrs: Either a ``range`` object or a single address.
"""
return (
b for b in self.byte_blocks_at(addrs) if isinstance(b, CodeBlock)
)
def data_blocks_on(
self, addrs: typing.Union[int, range]
) -> typing.Iterable[DataBlock]:
"""Finds all the data blocks that overlap an address or range of
addresses.
:param addrs: Either a ``range`` object or a single address.
"""
return (
b for b in self.byte_blocks_on(addrs) if isinstance(b, DataBlock)
)
def data_blocks_at(
self, addrs: typing.Union[int, range]
) -> typing.Iterable[DataBlock]:
"""Finds all the data blocks that begin at an address or range of
addresses.
:param addrs: Either a ``range`` object or a single address.
"""
return (
b for b in self.byte_blocks_at(addrs) if isinstance(b, DataBlock)
)
def symbolic_expressions_at(
self, addrs: typing.Union[int, range]
) -> typing.Iterable[SymbolicExpressionElement]:
"""Finds all the symbolic expressions that begin at an address or
range of addresses.
:param addrs: Either a ``range`` object or a single address.
:returns: Yields ``(interval, offset, symexpr)`` tuples for every
symbolic expression in the range.
"""
if self.address is None:
return
addrs = get_desired_range(addrs)
for i in self._symbolic_expressions._data.irange(
addrs.start - self.address,
addrs.stop - self.address,
inclusive=(True, False),
):
if self.address + i in addrs:
yield (self, i, self.symbolic_expressions[i])
def _add_to_uuid_cache(self, cache: typing.Dict[UUID, Node]) -> None:
"""Update the UUID cache when this node is added."""
cache[self.uuid] = self
for block in self.blocks:
block._add_to_uuid_cache(cache)
def _remove_from_uuid_cache(self, cache: typing.Dict[UUID, Node]) -> None:
"""Update the UUID cache when this node is removed."""
del cache[self.uuid]
for block in self.blocks:
block._remove_from_uuid_cache(cache)
@property
def module(self) -> typing.Optional["Module"]:
"""Get the module this node ultimately belongs to."""
if self.section is None:
return None
return self.section.module
@property
def ir(self) -> typing.Optional["IR"]:
"""Get the IR this node ultimately belongs to."""
if self.module is None:
return None
return self.module.ir
class IntervalGraph(object):
"""Base class for undirected interval graphs.
The IntervalGraph class allows any hashable object as a node
and can associate key/value attribute pairs with each undirected edge.
Each edge must have two integers, begin and end for its interval.
Self-loops are allowed but multiple edges
(two or more edges with the same nodes, begin and end interval) are not.
Two nodes can have more than one edge with different overlapping or non-overlapping intervals.
Parameters
----------
attr : keyword arguments, optional (default= no attributes)
Attributes to add to graph as key=value pairs.
Examples
--------
Create an empty graph structure (a "null interval graph") with no nodes and
no edges.
>>> G = dnx.IntervalGraph()
G can be grown in several ways.
**Nodes:**
Add one node at a time:
>>> G.add_node(1)
Add the nodes from any container (a list, dict, set or
even the lines from a file or the nodes from another graph).
Add the nodes from any container (a list, dict, set)
>>> G.add_nodes_from([2, 3])
>>> G.add_nodes_from(range(100, 110))
**Edges:**
G can also be grown by adding edges. This can be considered
the primary way to grow G, since nodes with no edge will not
appear in G in most cases. See ``G.to_snapshot()``.
Add one edge, which starts at 0 and ends at 10.
Keep in mind that the interval is [0, 10).
Thus, it does not include the end.
>>> G.add_edge(1, 2, 0, 10)
a list of edges,
>>> G.add_edges_from([(1, 2, 0, 10), (1, 3, 3, 11)])
If some edges connect nodes not yet in the graph, the nodes
are added automatically. There are no errors when adding
nodes or edges that already exist.
**Attributes:**
Each interval graph, node, and edge can hold key/value attribute pairs
in an associated attribute dictionary (the keys must be hashable).
By default these are empty, but can be added or changed using
add_edge, add_node.
Keep in mind that the edge interval is not an attribute of the edge.
>>> G = dnx.IntervalGraph(day="Friday")
>>> G.graph
{'day': 'Friday'}
Add node attributes using add_node(), add_nodes_from()
>>> G.add_node(1, time='5pm')
>>> G.add_nodes_from([3], time='2pm')
Add edge attributes using add_edge(), add_edges_from().
>>> G.add_edge(1, 2, 0, 10, weight=4.7 )
>>> G.add_edges_from([(3, 4, 3, 11), (4, 5, 0, 33)], color='red')
**Shortcuts:**
Here are a couple examples of available shortcuts:
>>> 1 in G # check if node in interval graph during any interval
True
>>> len(G) # number of nodes in the entire interval graph
5
**Subclasses (Advanced):**
Edges in interval graphs are represented by Interval Objects and are kept
in an IntervalTree. Both are based on
intervaltree available in pypi (https://pypi.org/project/intervaltree).
IntervalTree allows for fast interval based search through edges,
which makes interval graph analyes possible.
The Graph class uses a dict-of-dict-of-dict data structure.
The outer dict (node_dict) holds adjacency information keyed by node.
The next dict (adjlist_dict) represents the adjacency information and holds
edge data keyed by interval object. The inner dict (edge_attr_dict) represents
the edge data and holds edge attribute values keyed by attribute names.
"""
def __init__(self, **attr):
"""Initialize an interval graph with edges, name, or graph attributes.
Parameters
----------
attr : keyword arguments, optional (default= no attributes)
Attributes to add to graph as key=value pairs.
Examples
--------
>>> G = dnx.IntervalGraph()
>>> G = dnx.IntervalGraph(name='my graph')
>>> G.graph
{'name': 'my graph'}
"""
self.tree = IntervalTree()
self.graph = {} # dictionary for graph attributes
self._adj = {}
self._node = {}
self.graph.update(attr)
@property
def name(self):
"""String identifier of the interval graph.
This interval graph attribute appears in the attribute dict IG.graph
keyed by the string `"name"`. as well as an attribute (technically
a property) `IG.name`. This is entirely user controlled.
"""
return self.graph.get('name', '')
@name.setter
def name(self, s):
self.graph['name'] = s
def __str__(self):
"""Return the interval graph name.
Returns
-------
name : string
The name of the interval graph.
Examples
--------
>>> G = dnx.IntervalGraph(name='foo')
>>> str(G)
'foo'
"""
return self.name
def __len__(self):
"""Return the number of nodes. Use: 'len(G)'.
Returns
-------
nnodes : int
The number of nodes in the graph.
Examples
--------
>>> G = dnx.IntervalGraph()
>>> G.add_nodes_from([2, 4, 5])
>>> len(G)
3
"""
return len(self._node)
def __contains__(self, n):
"""Return True if n is a node, False otherwise. Use: 'n in G'.
Examples
--------
>>> G = dnx.IntervalGraph()
>>> G.add_node(2)
>>> 2 in G
True
"""
try:
return n in self._node
except TypeError:
return False
def interval(self):
"""Return a 2-tuple as (begin, end) interval of the entire
interval graph.
Note that end is non-inclusive.
Examples
--------
>>> G = dnx.IntervalGraph()
>>> G.add_edges_from([(1, 2, 0, 10), (3, 7, 9, 16)])
>>> G.interval()
(0, 16)
"""
return self.tree.begin(), self.tree.end()
def add_node(self, node_for_adding, **attr):
"""Add a single node `node_for_adding` and update node attributes.
Parameters
----------
node_for_adding : node
A node can be any hashable Python object except None.
attr : keyword arguments, optional
Set or change node attributes using key=value.
See Also
--------
add_nodes_from
Examples
--------
>>> G = dnx.IntervalGraph()
>>> G.add_node(1)
>>> G.add_node('Hello')
>>> G.number_of_nodes()
2
Use keywords set/change node attributes:
>>> G.add_node(1, size=10)
>>> G.add_node(3, weight=0.4, UTM=('13S', 382871, 3972649))
Notes
-----
A hashable object is one that can be used as a key in a Python
dictionary. This includes strings, numbers, tuples of strings
and numbers, etc.
On many platforms hashable items also include mutables such as
NetworkX Graphs, though one should be careful that the hash
doesn't change on mutables.
"""
if node_for_adding not in self._node:
self._adj[node_for_adding] = {}
self._node[node_for_adding] = attr
else: # update attr even if node already exists
self._node[node_for_adding].update(attr)
def add_nodes_from(self, nodes_for_adding, **attr):
"""Add multiple nodes.
Parameters
----------
nodes_for_adding : iterable container
A container of nodes (list, dict, set, etc.).
OR
A container of (node, attribute dict) tuples.
Node attributes are updated using the attribute dict.
attr : keyword arguments, optional (default= no attributes)
Update attributes for all nodes in nodes.
Node attributes specified in nodes as a tuple take
precedence over attributes specified via keyword arguments.
See Also
--------
add_node
Examples
--------
>>> G = dnx.IntervalGraph()
>>> G.add_nodes_from('Hello')
>>> G.has_node('e')
True
Use keywords to update specific node attributes for every node.
>>> G.add_nodes_from([1, 2], size=10)
>>> G.add_nodes_from([3, 4], weight=0.4)
Use (node, attrdict) tuples to update attributes for specific nodes.
>>> G.add_nodes_from([(1, dict(size=11)), (2, {'color':'blue'})])
"""
for n in nodes_for_adding:
# keep all this inside try/except because
# CPython throws TypeError on n not in self._node,
# while pre-2.7.5 ironpython throws on self._adj[n]
try:
if n not in self._node:
self._adj[n] = {}
self._node[n] = attr.copy()
else:
self._node[n].update(attr)
except TypeError:
nn, ndict = n
if nn not in self._node:
self._adj[nn] = {}
self._node[nn] = attr.copy()
self._node[nn].update(ndict)
else:
self._node[nn].update(attr)
self._node[nn].update(ndict)
def number_of_nodes(self, begin=None, end=None):
"""Return the number of nodes in the interval graph between the given interval.
Parameters
----------
begin: integer, optional (default= beginning of the entire interval graph)
Inclusive beginning time of the node appearing in the interval graph.
end: integer, optional (default= end of the entire interval graph + 1)
Non-inclusive ending time of the node appearing in the interval graph.
Must be bigger than begin.
Note that the default value is shifted up by 1 to make it an inclusive end.
Returns
-------
nnodes : int
The number of nodes in the interval graph.
See Also
--------
__len__
Examples
--------
>>> G = dnx.IntervalGraph()
>>> G.add_edges_from([(1, 2, 0, 5), (3, 4, 8, 11)])
>>> len(G)
4
>>> G.number_of_nodes()
4
>>> G.number_of_nodes(begin=6)
2
>>> G.number_of_nodes(begin=5, end=8) # end in non-inclusive
2
>>> G.number_of_nodes(end=8)
4
"""
if begin is None and end is None:
return len(self._node)
if begin is None:
begin = self.tree.begin()
if end is None:
end = self.tree.end() + 1
iedges = self.tree[begin:end]
inodes = set()
for iv in iedges:
inodes.add(iv.data[0])
inodes.add(iv.data[1])
return len(inodes)
def has_node(self, n, begin=None, end=None):
"""Return True if the interval graph contains the node n, during the given interval.
Identical to `n in G` when 'begin' and 'end' are not defined.
Parameters
----------
n : node
begin: integer, optional (default= beginning of the entire interval graph)
Inclusive beginning time of the node appearing in the interval graph.
end: integer, optional (default= end of the entire interval graph + 1)
Non-inclusive ending time of the node appearing in the interval graph.
Must be bigger than begin.
Note that the default value is shifted up by 1 to make it an inclusive end.
Examples
--------
>>> G = dnx.IntervalGraph()
>>> G.add_ndoe(1)
>>> G.has_node(1)
True
It is more readable and simpler to use
>>> 0 in G
True
With interval query:
>>> G.add_edge(3, 4, 2, 5)
>>> G.has_node(3)
True
>>> G.has_node(3, begin=2)
True
>>> G.has_node(3, end=2) # end is non-inclusive
False
"""
try:
exists_node = n in self._node
except TypeError:
exists_node = False
if (begin is None and end is None) or not exists_node:
return exists_node
if begin is None:
begin = self.tree.begin()
if end is None:
end = self.tree.end() + 1
iedges = self._adj[n].keys()
for iv in iedges:
if iv.overlaps(begin=begin, end=end):
return True
return False
def nodes(self, begin=None, end=None, data=False, default=None):
"""A NodeDataView of the IntervalGraph nodes.
A nodes is considered to be present during an interval, if it has
an edge with overlapping interval.
Parameters
----------
begin: integer, optional (default= beginning of the entire interval graph)
Inclusive beginning time of the node appearing in the interval graph.
end: integer, optional (default= end of the entire interval graph + 1)
Non-inclusive ending time of the node appearing in the interval graph.
Must be bigger than begin.
Note that the default value is shifted up by 1 to make it an inclusive end.
data : string or bool, optional (default=False)
The node attribute returned in 2-tuple (n, dict[data]).
If False, return just the nodes n.
default : value, optional (default=None)
Value used for nodes that don't have the requested attribute.
Only relevant if data is not True or False.
Returns
-------
NodeDataView
A NodeDataView iterates over `(n, data)` and has no set operations.
When called, if data is False, an iterator over nodes.
Otherwise an iterator of 2-tuples (node, attribute value)
where data is True.
Examples
--------
There are two simple ways of getting a list of all nodes in the graph:
>>> G = dnx.IntervalGraph()
>>> G.add_edges_from([(1, 2, 3, 10), (2, 4, 1, 11), (6, 4, 12, 19), (2, 4, 8, 15)])
[1, 2, 4, 6]
To get the node data along with the nodes:
>>> G.add_nodes_from([(1, {'time': '1pm'}), (2, {'time': '2pm'}), (4, {'time': '4pm'}), (6, {'day': 'Friday'})])
[(1, {'time': '1pm'}), (2, {'time': '2pm'}), (4, {'time': '4pm'}), (6, {'day': 'Friday'})]
>>> G.nodes(data="time")
[(1, '1pm'), (2, '2pm'), (4, '4pm'), (6, None)]
>>> G.nodes(data="time", default="5pm")
[(1, '1pm'), (2, '2pm'), (4, '4pm'), (6, '5pm')]
To get nodes which appear in a specific interval. nodes
without an edge are not considered present.
>>> G.nodes(begin=11, data=True)
[(2, {'time': '2pm'}), (4, {'time': '4pm'}), (6, {'day': 'Friday'})]
>>> G.nodes(begin=4, end=12) # non-inclusive end
[1, 2, 4]
"""
if begin is None and end is None:
return NodeDataView(self._node, data=data, default=default)
if begin is None:
begin = self.tree.begin()
if end is None:
end = self.tree.end() + 1
iedges = self.tree[begin:end]
inodes = set()
for iv in iedges:
inodes.add(iv.data[0])
inodes.add(iv.data[1])
node_dict = {n: self._node[n] for n in inodes}
return NodeDataView(node_dict, data=data, default=default)
def remove_node(self, n, begin=None, end=None):
"""Remove the presence of a node n within the given interval.
Removes the presence node n and all adjacent edges within the given interval.
If interval is specified, all the edges of n will be removed within that interval.
Quiet if n is not in the interval graph.
Parameters
----------
n : node
A node in the graph
begin: integer, optional (default= beginning of the entire interval graph)
Inclusive beginning time of the node appearing in the interval graph.
end: integer, optional (default= end of the entire interval graph + 1)
Non-inclusive ending time of the node appearing in the interval graph.
Must be bigger than begin.
Note that the default value is shifted up by 1 to make it an inclusive end.
Examples
--------
>>> G.add_edges_from([(1, 2, 3, 10), (2, 4, 1, 11), (6, 4, 12, 19), (2, 4, 8, 15)])
>>> G.add_nodes_from([(1, {'time': '1pm'}), (2, {'time': '2pm'}), (4, {'time': '4pm'})])
>>> G.nodes(begin=4, end=6)
[1, 2, 4, 6]
>>> G.remove_node(2, begin=4, end=6)
>>> G.nodes(begin=4, end=6)
[4, 6]
>>> G.nodes(data=True)
[(1, {'time': '1pm'}), (2, {'time': '2pm'}), (4, {'time': '4pm'}), (6, {})]
>>> G.remove_node(2)
>>> G.nodes(data=True)
[(1, {'time': '1pm'}), (4, {'time': '4pm'}), (6, {})]
"""
if n not in self._node:
return
if begin is None and end is None:
for iedge in list(self._adj[n].keys()):
self.__remove_iedge(iedge)
else:
if begin is None:
begin = self.tree.begin()
if end is None:
end = self.tree.end() + 1
for iedge in self.tree[begin:end]:
if iedge.data[0] == n or iedge.data[1] == n:
self.__remove_iedge(iedge)
# delete the node and its attributes if no edge left
if len(self._adj[n]) == 0:
self._adj.pop(n, None)
self._node.pop(n, None)
def add_edge(self, u, v, begin, end, **attr):
"""Add an edge between u and v, during interval [begin, end).
The nodes u and v will be automatically added if they are
not already in the interval graph.
Edge attributes can be specified with keywords or by directly
accessing the edge's attribute dictionary. See examples below.
Parameters
----------
u, v : nodes
Nodes can be, for example, strings or numbers.
Nodes must be hashable (and not None) Python objects.
begin: orderable type
Inclusive beginning time of the edge appearing in the interval graph.
end: orderable type
Non-inclusive ending time of the edge appearing in the interval graph.
Must be bigger than begin.
attr : keyword arguments, optional
Edge data (or labels or objects) can be assigned using
keyword arguments.
See Also
--------
add_edges_from : add a collection of edges
Notes
-----
Adding an edge that already exists updates the edge data.
Both begin and end must be the same type across all edges in the interval graph. Also, to create
snapshots, both must be integers.
Many NetworkX algorithms designed for weighted graphs use
an edge attribute (by default `weight`) to hold a numerical value.
Examples
--------
The following all add the edge e=(1, 2, 3, 10) to graph G:
>>> G = dnx.IntervalGraph()
>>> e = (1, 2, 3, 10)
>>> G.add_edge(1, 2, 3, 10) # explicit two-node form with interval
>>> G.add_edge(*e) # single edge as tuple of two nodes and interval
>>> G.add_edges_from([(1, 2, 3, 10)]) # add edges from iterable container
Associate data to edges using keywords:
>>> G.add_edge(1, 2, 3, 10 weight=3)
>>> G.add_edge(1, 3, 4, 9, weight=7, capacity=15, length=342.7)
"""
iedge = self.__get_iedge_in_tree(begin, end, u, v)
# if edge exists, just update attr
if iedge is not None:
# since both point to the same attr, updating one is enough
self._adj[u][iedge].update(attr)
return
iedge = Interval(begin, end, (u, v))
# add nodes
if u not in self._node:
self._adj[u] = {}
self._node[u] = {}
if v not in self._node:
self._adj[v] = {}
self._node[v] = {}
# add edge
try:
self.tree.add(iedge)
except ValueError:
raise NetworkXError(
"IntervalGraph: edge duration must be strictly bigger than zero {0}."
.format(iedge))
self._adj[u][iedge] = self._adj[v][iedge] = attr
def add_edges_from(self, ebunch_to_add, **attr):
"""Add all the edges in ebunch_to_add.
Parameters
----------
ebunch_to_add : container of edges
Each edge given in the container will be added to the
interval graph. The edges must be given as as 4-tuples (u, v, being, end).
Both begin and end must be orderable and the same type across all edges.
attr : keyword arguments, optional
Edge data (or labels or objects) can be assigned using
keyword arguments.
See Also
--------
add_edge : add a single edge
Notes
-----
Adding the same edge (with the same interval) twice has no effect
but any edge data will be updated when each duplicate edge is added.
Examples
--------
>>> G = dnx.IntervalGraph()
>>> G.add_edges_from([(1, 2, 3, 10), (2, 4, 1, 11)]) # using a list of edge tuples
Associate data to edges
>>> G.add_edges_from([(1, 2, 3, 10), (2, 4, 1, 11)], weight=3)
>>> G.add_edges_from([(3, 4, 2, 19), (1, 4, 1, 3)], label='WN2898')
"""
for e in ebunch_to_add:
if len(e) != 4:
raise NetworkXError(
"Edge tuple {0} must be a 4-tuple.".format(e))
self.add_edge(e[0], e[1], e[2], e[3], **attr)
def has_edge(self, u, v, begin=None, end=None, overlapping=True):
"""Return True if there exists an edge between u and v
in the interval graph, during the given interval.
Parameters
----------
u, v : nodes
Nodes can be, for example, strings or numbers.
Nodes must be hashable (and not None) Python objects.
begin : integer, optional (default= beginning of the entire interval graph)
Inclusive beginning time of the node appearing in the interval graph.
end : integer, optional (default= end of the entire interval graph + 1)
Non-inclusive ending time of the node appearing in the interval graph.
Must be bigger than begin.
Note that the default value is shifted up by 1 to make it an inclusive end.
overlapping : bool, optional (default= True)
if True, it returns True if there exists an edge between u and v with
overlapping interval with `begin` and `end`.
if False, it returns true only if there exists an edge between u and v
with the exact interval.
Note: if False, both `begin` and `end` must be defined, otherwise
an exception is raised.
Raises
------
NetworkXError
If `begin` and `end` are not defined and `overlapping= False`
Examples
--------
>>> G = dnx.IntervalGraph()
>>> G.add_edges_from([(1, 2, 3, 10), (2, 4, 1, 11)])
>>> G.has_edge(1, 2)
True
With specific overlapping interval:
>>> G.has_edge(1, 2, begin=2)
True
>>> G.has_edge(2, 4, begin=12)
False
Exact interval match:
>>> G.has_edge(2, 4, begin=1, end=11)
True
>>> G.has_edge(2, 4, begin=2, end=11)
False
"""
if begin is None and end is None:
for iv in self._adj[u].keys():
if iv.data[0] == v or iv.data[1] == v:
return True
return False
if not overlapping:
if begin is None or end is None:
raise NetworkXError(
"For exact interval match (overlapping=False), both begin and end must be defined."
)
return self.__get_iedge_in_tree(u, v, begin, end) is not None
if begin is None:
begin = self.tree.begin()
if end is None:
end = self.tree.end() + 1
for iv in self._adj[u].keys():
if (iv.data[0] == v or iv.data[1] == v) and iv.overlaps(
begin=begin, end=end):
return True
return False
def edges(self,
u=None,
v=None,
begin=None,
end=None,
data=False,
default=None):
"""A list of Interval objects of the IntervalGraph edges.
All edges which are present within the given interval.
All parameters are optional. `u` and `v` can be thought of as constraints.
If no node is defined, all edges within the interval are returned.
If one node is defined, all edges which have that node as one end,
will be returned, and finally if both nodes are defined then all
edges between the two nodes are returned.
Parameters
----------
u, v : nodes, optional (default=None)
Nodes can be, for example, strings or numbers.
Nodes must be hashable (and not None) Python objects.
If the node does not exist in the graph, a key error is raised.
begin: integer, optional (default= beginning of the entire interval graph)
Inclusive beginning time of the edge appearing in the interval graph.
end: integer, optional (default= end of the entire interval graph + 1)
Non-inclusive ending time of the edge appearing in the interval graph.
Must be bigger than begin.
Note that the default value is shifted up by 1 to make it an inclusive end.
data : string or bool, optional (default=False)
If True, return 2-tuple (Interval object, dict of attributes).
If False, return just the Interval objects.
If string (name of the attribute), return 2-tuple (Interval object, attribute value).
default : value, optional (default=None)
Default Value to be used for edges that don't have the requested attribute.
Only relevant if `data` is a string (name of an attribute).
Returns
-------
List of Interval objects
An interval object has the following format: (begin, end, (u, v))
When called, if `data` is False, a list of interval objects.
If `data` is True, a list of 2-tuples: (Interval, dict of attribute(s) with values),
If `data` is a string, a list of 2-tuples (Interval, attribute value).
Examples
--------
To get a list of all edges:
>>> G = dnx.IntervalGraph()
>>> G.add_edges_from([(1, 2, 3, 10), (2, 4, 1, 11), (6, 4, 12, 19), (2, 4, 8, 15)])
>>> G.edges()
[Interval(8, 15, (2, 4)), Interval(3, 10, (1, 2)), Interval(1, 11, (2, 4)), Interval(12, 19, (6, 4))]
To get edges which appear in a specific interval:
>>> G.edges(begin=10)
[Interval(12, 19, (6, 4)), Interval(1, 11, (2, 4)), Interval(8, 15, (2, 4))]
>>> G.edges(end=5)
[Interval(3, 10, (1, 2)), Interval(1, 11, (2, 4))]
>>> G.edges(begin=2, end=4)
[Interval(3, 10, (1, 2)), Interval(1, 11, (2, 4))]
To get edges with either of the two nodes being defined:
>>> G.edges(u=2)
[Interval(3, 10, (1, 2)), Interval(1, 11, (2, 4)), Interval(8, 15, (2, 4))]
>>> G.edges(u=2, begin=11)
[Interval(1, 11, (2, 4)), Interval(8, 15, (2, 4))]
>>> G.edges(u=2, v=4, end=8)
[Interval(1, 11, (2, 4))]
>>> G.edges(u=1, v=6)
[]
To get a list of edges with data:
>>> G = dnx.IntervalGraph()
>>> G.add_edge(1, 3, 1, 4, weight=8, height=18)
>>> G.add_edge(1, 2, 3, 10, weight=10)
>>> G.add_edge(2, 6, 2, 10)
>>> G.edges(data="weight")
[(Interval(2, 8, (2, 3)), None), (Interval(3, 10, (1, 2)), 10), (Interval(1, 4, (1, 3)), 8)]
>>> G.edges(data="weight", default=5)
[(Interval(2, 8, (2, 3)), 5), (Interval(3, 10, (1, 2)), 10), (Interval(1, 4, (1, 3)), 8)]
>>> G.edges(data=True)
[(Interval(2, 8, (2, 3)), {}), (Interval(3, 10, (1, 2)), {'weight': 10}), (Interval(1, 4, (1, 3)), {'height': 18, 'weight': 8})]
>>> G.edges(u=1, begin=5, end=9, data="weight")
[(Interval(3, 10, (1, 2)), 10)]
"""
# If non of the nodes are defined the interval tree is queried for the list of edges,
# otherwise the edges are returned based on the nodes in the self._adj.o
if u is None and v is None:
if begin is None and end is None:
iedges = self.tree.all_intervals
# interval filtering
else:
if begin is None:
begin = self.tree.begin()
if end is None:
end = self.tree.end() + 1
iedges = self.tree[begin:end]
else:
# Node filtering
if u is not None and v is not None:
iedges = [
iv for iv in self._adj[u].keys()
if iv.data[0] == v or iv.data[1] == v
]
elif u is not None:
iedges = self._adj[u].keys()
else:
iedges = self._adj[v].keys()
# Interval filtering
if begin is not None and end is not None:
iedges = [
iv for iv in iedges if iv.end >= begin and iv.begin < end
]
elif begin is not None:
iedges = [iv for iv in iedges if iv.end >= begin]
elif end is not None:
iedges = [iv for iv in iedges if iv.begin < end]
# Appending attribute data if needed
if data is False:
return iedges if isinstance(iedges, list) else list(iedges)
if data is True:
return [(iv, self._adj[iv.data[0]][iv]) for iv in iedges]
return [(iv, self._adj[iv.data[0]][iv][data])
if data in self._adj[iv.data[0]][iv].keys() else (iv, default)
for iv in iedges]
def remove_edge(self, u, v, begin=None, end=None, overlapping=True):
"""Remove the edge between u and v in the interval graph,
during the given interval.
Quiet if the specified edge is not present.
Parameters
----------
u, v : nodes
Nodes can be, for example, strings or numbers.
Nodes must be hashable (and not None) Python objects.
begin : integer, optional (default= beginning of the entire interval graph)
Inclusive beginning time of the edge appearing in the interval graph.
end : integer, optional (default= end of the entire interval graph + 1)
Non-inclusive ending time of the edge appearing in the interval graph.
Must be bigger than begin.
Note that the default value is shifted up by 1 to make it an inclusive end.
overlapping : bool, optional (default= True)
if True, remove the edge between u and v with overlapping interval
with `begin` and `end`.
if False, remove the edge between u and v with the exact interval.
Note: if False, both `begin` and `end` must be defined, otherwise
an exception is raised.
Raises
------
NetworkXError
If `begin` and `end` are not defined and `overlapping= False`
Examples
--------
>>> G = dnx.IntervalGraph()
>>> G.add_edges_from([(1, 2, 3, 10), (2, 4, 1, 11), (6, 4, 5, 9), (1, 2, 8, 15)])
>>> G.remove_edge(1, 2)
>>> G.has_edge(1, 2)
False
With specific overlapping interval
>>> G = dnx.IntervalGraph()
>>> G.add_edges_from([(1, 2, 3, 10), (2, 4, 1, 11), (6, 4, 5, 9), (1, 2, 8, 15)])
>>> G.remove_edge(1, 2, begin=2, end=4)
>>> G.has_edge(1, 2, begin=2, end=4)
False
>>> G.has_edge(1, 2)
True
Exact interval match
>>> G.remove_edge(2, 4, begin=1, end=11, overlapping=False)
>>> G.has_edge(2, 4, begin=1, end=11)
False
"""
# remove edge between u and v with the exact given interval
if not overlapping:
if begin is None or end is None:
raise NetworkXError(
"For exact interval match (overlapping=False), both begin and end must be defined."
)
iedge = self.__get_iedge_in_tree(u, v, begin, end)
if iedge is None:
return
self.__remove_iedge(iedge)
return
iedges_to_remove = []
# remove every edge between u and v
if begin is None and end is None:
for iv in self._adj[u].keys():
if iv.data[0] == v or iv.data[1] == v:
iedges_to_remove.append(iv)
# remove edge between u and v with overlapping interval with the given interval
if begin is None:
begin = self.tree.begin()
if end is None:
end = self.tree.end() + 1
for iv in self._adj[u].keys():
if (iv.data[0] == v or iv.data[1] == v) and iv.overlaps(
begin=begin, end=end):
iedges_to_remove.append(iv)
# removing found iedges
for iv in iedges_to_remove:
self.__remove_iedge(iv)
def __remove_iedge(self, iedge):
"""Remove the interval edge from the interval graph.
Quiet if the specified edge is not present.
Parameters
----------
iedge : Interval object
Interval edge to be removed.
Examples
--------
>>> G = dnx.IntervalGraph()
>>> G.add_edge(1, 2, 3, 10)
>>> iedge = Interval(3, 10, (1, 2)) # Interval(begin, end, (u, v))
>>> G.__remove_iedge(iedge)
"""
self.tree.discard(iedge)
self._adj[iedge.data[0]].pop(iedge, None)
self._adj[iedge.data[1]].pop(iedge, None)
def __get_iedge_in_tree(self, u, v, begin, end):
"""Return interval edge if found in the interval graph with the exact interval,
otherwise return None.
Parameters
----------
u, v : nodes
Nodes can be, for example, strings or numbers.
Nodes must be hashable (and not None) Python objects.
begin : integer
Inclusive beginning time of the edge appearing in the interval graph.
end : integer
Non-inclusive ending time of the edge appearing in the interval graph.
Must be bigger than begin.
Examples
--------
>>> G = dnx.IntervalGraph()
>>> G.add_edge(1, 2, 3, 10)
>>> G.__get_iedge_in_tree(2, 1, 3, 10)
Interval(3, 10, (1, 2))
>>> G.__get_iedge_in_tree(2, 1, 4, 10)
None
"""
temp_iedge = Interval(begin, end, (u, v))
if temp_iedge in self.tree:
return temp_iedge
temp_iedge = Interval(begin, end, (v, u))
if temp_iedge in self.tree:
return temp_iedge
return None
def to_subgraph(self,
begin,
end,
multigraph=False,
edge_data=False,
edge_interval_data=False,
node_data=False):
"""Return a networkx Graph or MultiGraph which includes all the nodes and
edges which have overlapping intervals with the given interval.
Parameters
----------
begin: integer
Inclusive beginning time of the edge appearing in the interval graph.
Must be bigger than begin.
end: integer
Non-inclusive ending time of the edge appearing in the interval graph.
multigraph: bool, optional (default= False)
If True, a networkx MultiGraph will be returned. If False, networkx Graph.
edge_data: bool, optional (default= False)
If True, edges will keep their attributes.
edge_interval_data: bool, optional (default= False)
If True, each edge's attribute will also include its begin and end interval data.
If `edge_data= True` and there already exist edge attributes with names begin and end,
they will be overwritten.
node_data : bool, optional (default= False)
if True, each node's attributes will be included.
See Also
--------
to_snapshots : divide the interval graph to snapshots
Notes
-----
If multigraph= False, and edge_data=True or edge_interval_data=True,
in case there are multiple edges, only one will show with one of the edge's attributes.
Note: nodes with no edges will not appear in any subgraph.
Examples
--------
>>> G = dnx.IntervalGraph()
>>> G.add_edges_from([(1, 2, 3, 10), (2, 4, 1, 11), (6, 4, 12, 19), (2, 4, 8, 15)])
>>> H = G.to_subgraph(4, 12)
>>> type(H)
<class 'networkx.classes.graph.Graph'>
>>> list(H.edges(data=True))
[(1, 2, {}), (2, 4, {})]
>>> H = G.to_subgraph(4, 12, edge_interval_data=True)
>>> type(H)
<class 'networkx.classes.graph.Graph'>
>>> list(H.edges(data=True))
[(1, 2, {'end': 10, 'begin': 3}), (2, 4, {'end': 15, 'begin': 8})]
>>> M = G.to_subgraph(4, 12, multigraph=True, edge_interval_data=True)
>>> type(M)
<class 'networkx.classes.multigraph.MultiGraph'>
>>> list(M.edges(data=True))
[(1, 2, {'end': 10, 'begin': 3}), (2, 4, {'end': 11, 'begin': 1}), (2, 4, {'end': 15, 'begin': 8})]
"""
if end <= begin:
raise NetworkXError(
"IntervalGraph: subgraph duration must be strictly bigger than zero: "
"begin: {}, end: {}.".format(begin, end))
iedges = self.tree[begin:end]
if multigraph:
G = MultiGraph()
else:
G = Graph()
if edge_data and edge_interval_data:
G.add_edges_from((iedge.data[0], iedge.data[1],
dict(self._adj[iedge.data[0]][iedge],
begin=iedge.begin,
end=iedge.end)) for iedge in iedges)
elif edge_data:
G.add_edges_from((iedge.data[0], iedge.data[1],
self._adj[iedge.data[0]][iedge].copy())
for iedge in iedges)
elif edge_interval_data:
G.add_edges_from((iedge.data[0], iedge.data[1], {
'begin': iedge.begin,
'end': iedge.end
}) for iedge in iedges)
else:
G.add_edges_from(
(iedge.data[0], iedge.data[1]) for iedge in iedges)
# include node attributes
if node_data:
G.add_nodes_from((n, self._node[n].copy()) for n in G.nodes)
return G
def to_snapshots(self,
number_of_snapshots,
multigraph=False,
edge_data=False,
edge_interval_data=False,
node_data=False,
return_length=False):
"""Return a list of networkx Graph or MultiGraph objects as snapshots
of the interval graph in consecutive order.
Parameters
----------
number_of_snapshots : integer
Number of snapshots to divide the interval graph into.
Must be bigger than 1.
multigraph : bool, optional (default= False)
If True, a networkx MultiGraph will be returned. If False, networkx Graph.
edge_data: bool, optional (default= False)
If True, edges will keep their attributes.
edge_interval_data : bool, optional (default= False)
If True, each edge's attribute will also include its begin and end interval data.
If `edge_data= True` and there already exist edge attributes with names begin and end,
they will be overwritten.
node_data : bool, optional (default= False)
if True, each node's attributes will be included.
return_length : bool, optional (default= False)
If true, the length of snapshots will be returned as the second argument.
See Also
--------
to_subgraph : subgraph based on an interval
Notes
-----
In order to create snapshots, begin and end interval objects of the interval graph must be numbers.
If multigraph= False, and edge_data=True or edge_interval_data=True,
in case there are multiple edges, only one will show with one of the edge's attributes.
Examples
--------
Snapshots of NetworkX Graph
>>> G = dnx.IntervalGraph()
>>> G.add_edges_from([(1, 2, 3, 10), (2, 4, 1, 11), (6, 4, 12, 19), (2, 4, 8, 15)])
>>> S, l = G.to_snapshots(2, edge_interval_data=True, return_length=True)
>>> S
[<networkx.classes.graph.Graph object at 0x100000>, <networkx.classes.graph.Graph object at 0x150d00>]
>>> l
9.0
>>> for g in S:
>>> ... g.edges(data=True))
[(1, 2, {'begin': 3, 'end': 10}), (2, 4, {'begin': 8, 'end': 15})]
[(2, 4, {'begin': 8, 'end': 15}), (4, 6, {'begin': 12, 'end': 19})]
Snapshots of NetworkX MultiGraph
>>> S, l = G.to_snapshots(3, multigraph=True, edge_interval_data=True, return_length=True)
>>> S
[<networkx.classes.multigraph.MultiGraph object at 0x1060d40b8>, <networkx.classes.multigraph.MultiGraph object at 0x151020c9e8>, <networkx.classes.multigraph.MultiGraph object at 0x151021d390>]
>>> l
6.0
>>> for g in S:
>>> ... g.edges(data=True))
[(1, 2, {'end': 10, 'begin': 3}), (2, 4, {'end': 11, 'begin': 1})]
[(1, 2, {'end': 10, 'begin': 3}), (2, 4, {'end': 11, 'begin': 1}), (2, 4, {'end': 15, 'begin': 8}), (4, 6, {'end': 19, 'begin': 12})]
[(2, 4, {'end': 15, 'begin': 8}), (4, 6, {'end': 19, 'begin': 12})]
"""
if number_of_snapshots < 2 or type(number_of_snapshots) is not int:
raise NetworkXError(
"IntervalGraph: number of snapshots must be an integer and 2 or bigger. "
"{0} was passed.".format(number_of_snapshots))
begin, end = self.interval()
snapshot_len = (end - begin) / number_of_snapshots
snapshots = []
end_inclusive_addition = 0
for i in range(number_of_snapshots):
# since to_subgraph is end non-inclusive, shift the end up by 1 to include end in the last snapshot.
if i == number_of_snapshots - 1:
end_inclusive_addition = 1
snapshots.append(
self.to_subgraph(begin + snapshot_len * i,
begin + snapshot_len * (i + 1) +
end_inclusive_addition,
multigraph=multigraph,
edge_data=edge_data,
edge_interval_data=edge_interval_data,
node_data=node_data))
if return_length:
return snapshots, snapshot_len
return snapshots
@staticmethod
def load_from_txt(path, delimiter=" ", nodetype=None, comments="#"):
"""Read interval graph in from path.
Every line in the file must be an edge in the following format: "node node begin end".
Both interval times must be integers. Nodes can be any hashable objects.
Parameters
----------
path : string or file
Filename to read.
nodetype : Python type, optional
Convert nodes to this type.
comments : string, optional
Marker for comment lines
delimiter : string, optional
Separator for node labels. The default is whitespace.
Returns
-------
G: IntervalGraph
The graph corresponding to the lines in edge list.
Examples
--------
>>> G=dnx.IntervalGraph.load_from_txt("my_dygraph.txt")
The optional nodetype is a function to convert node strings to nodetype.
For example
>>> G=dnx.IntervalGraph.load_from_txt("my_dygraph.txt", nodetype=int)
will attempt to convert all nodes to integer type.
Since nodes must be hashable, the function nodetype must return hashable
types (e.g. int, float, str, frozenset - or tuples of those, etc.)
"""
ig = IntervalGraph()
with open(path, 'r') as file:
for line in file:
p = line.find(comments)
if p >= 0:
line = line[:p]
if not len(line):
continue
line = line.rstrip().split(delimiter)
u, v, begin, end = line
if nodetype is not None:
try:
u = nodetype(u)
v = nodetype(v)
except:
raise TypeError(
"Failed to convert node to type {0}".format(
nodetype))
try:
begin = int(begin)
end = nodetype(end)
except:
raise TypeError("Failed to convert time to type int")
ig.add_edge(u, v, begin, end)
return ig
def _remove_overlaps(self, position_idy: IntervalTree, percents: dict):
while len(position_idy) > 0:
item = position_idy.pop()
start = item.begin
end = item.end
cat = item.data
overlaps = position_idy.search(start, end)
if len(overlaps) > 0:
has_overlap = False
for overlap in overlaps:
if has_overlap:
break
o_start = overlap.begin
o_end = overlap.end
o_cat = overlap.data
if not position_idy.containsi(o_start, o_end, o_cat):
continue
if start < o_start:
if end <= o_end:
# cccccccccccccc*******
# *****ooooooooo[ooooooo]
if o_cat < cat:
if end < o_end:
# No overlap with the current item, we stay has_overlap as False
position_idy.discard(overlap)
position_idy[end:o_end] = o_cat
else:
position_idy.discard(
overlap) # No kept overlap
elif o_cat == cat:
if end < o_end:
has_overlap = True
position_idy.discard(overlap)
position_idy[start:o_end] = cat
else:
position_idy.discard(
overlap) # No kept overlap
else:
has_overlap = True
position_idy.discard(overlap)
position_idy[start:o_start] = cat
position_idy[o_start:o_end] = o_cat
else: # end > o_end
# ccccccccccccccccccc
# *****oooooooooo****
if o_cat <= cat:
position_idy.discard(
overlap) # No kept overlap
else: # o_cat > cat
has_overlap = True
position_idy.discard(overlap)
position_idy[start:o_start] = cat
position_idy[o_start:o_end] = o_cat
position_idy[o_end:end] = cat
elif start == o_start:
if end < o_end:
# cccccccccccc*******
# ooooooooooooooooooo
if o_cat < cat:
# No overlap with the current item, we stay has_overlap as False
position_idy.discard(overlap)
position_idy[end:o_end] = o_cat
elif o_cat == cat:
has_overlap = True
position_idy.discard(overlap)
position_idy[start:o_end] = cat
else: # o_cat > cat
# The overlap just contains current item
has_overlap = True
elif end == o_end:
# ***cccccccccccccccc***
# ***oooooooooooooooo***
if o_cat <= cat:
position_idy.discard(
overlap) # No kept overlap
else:
# The overlap just contains current item
has_overlap = True
else: # end > o_end
# ccccccccccccccccccccccccccccc
# oooooooooooooooooooo*********
if o_cat <= cat:
# current item just contains the overlap
position_idy.discard(overlap)
else:
has_overlap = True
position_idy.discard(overlap)
position_idy[o_start:o_end] = o_cat
position_idy[o_end:end] = cat
else: # start > o_start
if end <= o_end:
# ******ccccccccc*******
# ooooooooooooooo[ooooooo]
if o_cat < cat:
has_overlap = True
position_idy.discard(overlap)
position_idy[o_start:start] = o_cat
position_idy[start:end] = cat
if end < o_end:
position_idy[end:o_end] = o_cat
else: # o_cat >= cat
# Overlap just contains the item
has_overlap = True
else: # end > o_end
# ******ccccccccccccccccccccc
# ooooooooooooooooo**********
if o_cat < cat:
has_overlap = True
position_idy.discard(overlap)
position_idy[o_start:start] = o_cat
position_idy[start:end] = cat
elif o_cat == cat:
has_overlap = True
position_idy.discard(overlap)
position_idy[o_start:end] = cat
else: # o_cat > cat
has_overlap = True
position_idy[o_end:end] = cat
if not has_overlap:
percents = self._add_percents(percents, item)
else:
percents = self._add_percents(percents, item)
return percents
target_mem_end = address + size
if (target_mem_beg,
target_mem_end) in address_to_check_for_USE.keys():
for item in address_to_check_for_USE[(
target_mem_beg, target_mem_end)].keys():
if len(item) == 2:
beg, end = item
for node in DEF_list[beg:end]:
def_beg, def_end = node.begin, node.end
def_func, def_var, def_line = node.data
# local variables
if def_func not in call_stack and len(
LOCAL_list[def_func]
[def_beg:def_end]) != 0:
DEF_list.discard(node)
address_to_check_for_USE.pop(
(def_beg, def_end), None)
for key in address_to_check_for_USE.keys():
address_to_check_for_USE[key].pop(
(def_beg, def_end), None)
functions_to_remove[def_func] = True
elif tag == "DEF" and (beg, end) == (
target_mem_beg, target_mem_end):
if def_beg == beg and end == def_end:
DEF_list.discard(node)
address_to_check_for_USE.pop(
(def_beg, def_end), None)
for key in address_to_check_for_USE.keys(
):
address_to_check_for_USE[key].pop(
def test_all():
from intervaltree import Interval, IntervalTree
from pprint import pprint
from operator import attrgetter
def makeinterval(lst):
return Interval(
lst[0],
lst[1],
"{}-{}".format(*lst)
)
ivs = list(map(makeinterval, [
[1,2],
[4,7],
[5,9],
[6,10],
[8,10],
[8,15],
[10,12],
[12,14],
[14,15],
]))
t = IntervalTree(ivs)
t.verify()
def data(s):
return set(map(attrgetter('data'), s))
# Query tests
print('Query tests...')
assert data(t[4]) == set(['4-7'])
assert data(t[4:5]) == set(['4-7'])
assert data(t[4:6]) == set(['4-7', '5-9'])
assert data(t[9]) == set(['6-10', '8-10', '8-15'])
assert data(t[15]) == set()
assert data(t.search(5)) == set(['4-7', '5-9'])
assert data(t.search(6, 11, strict = True)) == set(['6-10', '8-10'])
print(' passed')
# Membership tests
print('Membership tests...')
assert ivs[1] in t
assert Interval(1,3, '1-3') not in t
assert t.overlaps(4)
assert t.overlaps(9)
assert not t.overlaps(15)
assert t.overlaps(0,4)
assert t.overlaps(1,2)
assert t.overlaps(1,3)
assert t.overlaps(8,15)
assert not t.overlaps(15, 16)
assert not t.overlaps(-1, 0)
assert not t.overlaps(2,4)
print(' passed')
# Insertion tests
print('Insertion tests...')
t.add( makeinterval([1,2]) ) # adding duplicate should do nothing
assert data(t[1]) == set(['1-2'])
t[1:2] = '1-2' # adding duplicate should do nothing
assert data(t[1]) == set(['1-2'])
t.add(makeinterval([2,4]))
assert data(t[2]) == set(['2-4'])
t.verify()
t[13:15] = '13-15'
assert data(t[14]) == set(['8-15', '13-15', '14-15'])
t.verify()
print(' passed')
# Duplication tests
print('Interval duplication tests...')
t.add(Interval(14,15,'14-15####'))
assert data(t[14]) == set(['8-15', '13-15', '14-15', '14-15####'])
t.verify()
print(' passed')
# Copying and casting
print('Tree copying and casting...')
tcopy = IntervalTree(t)
tcopy.verify()
assert t == tcopy
tlist = list(t)
for iv in tlist:
assert iv in t
for iv in t:
assert iv in tlist
tset = set(t)
assert tset == t.items()
print(' passed')
# Deletion tests
print('Deletion tests...')
try:
t.remove(
Interval(1,3, "Doesn't exist")
)
except ValueError:
pass
else:
raise AssertionError("Expected ValueError")
try:
t.remove(
Interval(500, 1000, "Doesn't exist")
)
except ValueError:
pass
else:
raise AssertionError("Expected ValueError")
orig = t.print_structure(True)
t.discard( Interval(1,3, "Doesn't exist") )
t.discard( Interval(500, 1000, "Doesn't exist") )
assert data(t[14]) == set(['8-15', '13-15', '14-15', '14-15####'])
t.remove( Interval(14,15,'14-15####') )
assert data(t[14]) == set(['8-15', '13-15', '14-15'])
t.verify()
assert data(t[2]) == set(['2-4'])
t.discard( makeinterval([2,4]) )
assert data(t[2]) == set()
t.verify()
assert t[14]
t.remove_overlap(14)
t.verify()
assert not t[14]
# Emptying the tree
#t.print_structure()
for iv in sorted(iter(t)):
#print('### Removing '+str(iv)+'... ###')
t.remove(iv)
#t.print_structure()
t.verify()
#print('')
assert len(t) == 0
assert t.is_empty()
assert not t
t = IntervalTree(ivs)
#t.print_structure()
t.remove_overlap(1)
#t.print_structure()
t.verify()
t.remove_overlap(8)
#t.print_structure()
print(' passed')
t = IntervalTree(ivs)
pprint(t)
t.split_overlaps()
pprint(t)
#import cPickle as pickle
#p = pickle.dumps(t)
#print(p)
