def _read_ts_json(data, discrete, instantaneous, merge):
instantaneous = ('tf' not in data
if instantaneous is None else instantaneous)
if instantaneous:
return ITimeSetS(data['ts'], discrete=discrete, no_duplicates=merge)
else:
return TimeSetDF(DF(data), discrete=discrete, disjoint_intervals=merge)
def timeset_df(self):
from stream_graph import TimeSetDF
times = list(self.times_)
# Cast to a TimeSetDF with the same start and finish
return TimeSetDF(pd.DataFrame({
'ts': times,
'tf': times
}),
discrete=self.discrete)
def substream(self, nsu=None, nsv=None, ts=None):
if nsu is not None:
if not isinstance(nsu, ABC.NodeSet):
try:
nsu = NodeSetS(nsu)
except Exception as ex:
raise UnrecognizedNodeSet('nsu: ' + str(ex))
if nsv is not None:
if not isinstance(nsv, ABC.NodeSet):
try:
nsv = NodeSetS(nsv)
except Exception as ex:
raise UnrecognizedNodeSet('nsv: ' + str(ex))
if ts is not None:
if not isinstance(ts, ABC.TimeSet):
try:
ts = list(ts)
if any(isinstance(t, Iterable) for t in ts):
from stream_graph import TimeSetDF
ts = TimeSetDF(ts, discrete=self.timeset_.discrete)
else:
from stream_graph import ITimeSetS
ts = ITimeSetS(ts, discrete=self.timeset_.discrete)
except Exception as ex:
raise UnrecognizedTimeSet('ts: ' + ex)
if all(o is None for o in [nsu, nsv, ts]):
return self.copy()
if nsu is not None and nsv is not None:
ns = nsu | nsv
elif nsu is not None:
ns = nsu
elif nsv is not None:
ns = nsv
else:
ns = None
# Build the new nodeset
nodeset = (self.nodeset if ns is None else self.nodeset_ & ns)
# Build the new timeset
timeset = (self.timeset if ts is None else self.timeset_ & ts)
# Build the new temporal-nodeset
temporal_nodeset = self.temporal_nodeset_.substream(nsu=ns, ts=ts)
# Build the new temporal-nodeset
temporal_linkset = self.temporal_linkset_.substream(nsu=nsu,
nsv=nsv,
ts=ts)
return self.__class__(nodeset, timeset, temporal_nodeset,
temporal_linkset)
def __init__(self,
nodeset=None,
timeset=None,
temporal_nodeset=None,
temporal_linkset=None,
discrete=None,
weighted=False):
if not isinstance(nodeset, ABC.NodeSet):
from . import NodeSetS
self.nodeset_ = NodeSetS(nodeset)
else:
self.nodeset_ = nodeset
if not isinstance(timeset, ABC.TimeSet):
from .time_set_df import TimeSetDF
if discrete is None:
discrete = False
self.timeset_ = TimeSetDF(timeset, discrete=discrete)
else:
self.timeset_ = timeset
discrete = timeset.discrete
if not isinstance(temporal_nodeset, ABC.TemporalNodeSet):
from . import TemporalNodeSetDF
self.temporal_nodeset_ = TemporalNodeSetDF(
temporal_nodeset, discrete=self.timeset_.discrete)
else:
self.temporal_nodeset_ = temporal_nodeset
assert self.timeset_.discrete is None or self.timeset_.discrete == self.temporal_nodeset_.discrete
if not isinstance(temporal_linkset, ABC.TemporalLinkSet):
from . import TemporalLinkSetDF
self.temporal_linkset_ = TemporalLinkSetDF(
temporal_linkset,
discrete=self.timeset_.discrete,
weighted=weighted)
else:
self.temporal_linkset_ = temporal_linkset
assert self.timeset_.discrete is None or self.timeset_.discrete == self.temporal_linkset_.discrete
def substream(self, nsu=None, ts=None):
if nsu is not None:
if not isinstance(nsu, ABC.NodeSet):
try:
nsu = NodeSetS(nsu)
except Exception as ex:
raise UnrecognizedNodeSet('nsu: ' + ex)
if ts is not None:
# check if we have a valid nodeset or try to cast
if not isinstance(ts, ABC.TimeSet):
try:
ts = list(ts)
if any(isinstance(t, Iterable) for t in ts):
from stream_graph import TimeSetDF
ts = TimeSetDF(ts, discrete=self.discrete)
else:
ts = ITimeSetS(ts, discrete=self.discrete)
except Exception as ex:
raise UnrecognizedTimeSet('ts: ' + ex)
if all(o is None for o in [nsu, ts]):
return self.copy()
if bool(self) and all((o is None or bool(o)) for o in [nsu, ts]):
if nsu is not None:
df = self.df[self.df.u.isin(nsu)]
else:
df = self.df
if ts is not None:
if ts.instantaneous:
# if instantaneous, intersect each key with the same time-stamps.
df = df.intersection(set(ts),
by_key=False,
on_column=['u', 'v'])
else:
ts = list(
ts_to_df(ts).sort_values(by='ts').itertuples(
index=False, name=None))
# keep valid instant by doing a binary search on intervals.
df = [
key for key in df.itertuples()
if t_in(ts, key[1], 0,
len(ts) - 1)
]
return self.__class__(df, discrete=self.discrete)
else:
return self.__class__()
class StreamGraph(object):
""" StreamGraph
A StreamGraph :math:`S=(T, V, W, E)` is a collection of four elements:
- :math:`V`, a node-set
- :math:`T`, a time-set
- :math:`W\\subseteq T \\times V`, a temporal-node-set
- :math:`E\\subseteq T \\times V \\otimes V`, a temporal-link-set
Parameters:
-----------
nodeset: ABC.NodeSet
timeset: ABC.TimeSet or ABC.ITimeSet
temporal_nodeset: ABC.TemporalNodeSet or ABC.ITemporalNodeSet
temporal_linkset: ABC.TemporalLinkSet or ABC.ITemporalLinkSet
"""
def __init__(self,
nodeset=None,
timeset=None,
temporal_nodeset=None,
temporal_linkset=None,
discrete=None,
weighted=False):
if not isinstance(nodeset, ABC.NodeSet):
from . import NodeSetS
self.nodeset_ = NodeSetS(nodeset)
else:
self.nodeset_ = nodeset
if not isinstance(timeset, ABC.TimeSet):
from .time_set_df import TimeSetDF
if discrete is None:
discrete = False
self.timeset_ = TimeSetDF(timeset, discrete=discrete)
else:
self.timeset_ = timeset
discrete = timeset.discrete
if not isinstance(temporal_nodeset, ABC.TemporalNodeSet):
from . import TemporalNodeSetDF
self.temporal_nodeset_ = TemporalNodeSetDF(
temporal_nodeset, discrete=self.timeset_.discrete)
else:
self.temporal_nodeset_ = temporal_nodeset
assert self.timeset_.discrete is None or self.timeset_.discrete == self.temporal_nodeset_.discrete
if not isinstance(temporal_linkset, ABC.TemporalLinkSet):
from . import TemporalLinkSetDF
self.temporal_linkset_ = TemporalLinkSetDF(
temporal_linkset,
discrete=self.timeset_.discrete,
weighted=weighted)
else:
self.temporal_linkset_ = temporal_linkset
assert self.timeset_.discrete is None or self.timeset_.discrete == self.temporal_linkset_.discrete
def __bool__(self):
return ((hasattr(self, 'nodeset_') and bool(self.nodeset_))
and (hasattr(self, 'timeset_') and bool(self.timeset_))
and (hasattr(self, 'temporal_nodeset_')
and bool(self.temporal_nodeset_))
and (hasattr(self, 'temporal_linkset_')
and bool(self.temporal_linkset_)))
# Python2 cross-compatibility
__nonzero__ = __bool__
def __str__(self):
if bool(self):
out = [('Node-Set', str(self.nodeset_))]
out += [('Time-Set', str(self.timeset_))]
out += [('Temporal-Node-Set', str(self.temporal_nodeset_))]
out += [('Temporal-Link-Set', str(self.temporal_linkset_))]
header = ['Stream-Graph']
header += [len(header[0]) * '=']
return '\n\n'.join(
['\n'.join(header)] +
['\n'.join([a, len(a) * '-', b]) for a, b in out])
else:
out = ["Empty Stream-Graph"]
out = [out[0] + "\n" + len(out[0]) * '-']
if not hasattr(self, 'nodeset_'):
out += ['- Node-Set: None']
elif not bool(self.nodeset_):
out += ['- Node-Set: Empty']
if not hasattr(self, 'timeset_'):
out += ['- Time-Set: None']
elif not bool(self.timeset_):
out += ['- Time-Set: Empty']
if not hasattr(self, 'temporal_nodeset_'):
out += ['- Temporal-Node-Set: None']
elif not bool(self.temporal_nodeset_):
out += ['- Temporal-Node-Set: Empty']
if not hasattr(self, 'temporal_linkset_'):
out += ['- Temporal-Link-Set: None']
elif not bool(self.temporal_linkset_):
out += ['- Temporal-Link-Set: Empty']
return '\n\n '.join(out)
@property
def weighted(self):
return self.temporal_linkset_.weighted
@property
def discrete(self):
return self.timeset_.discrete
@property
def nodeset(self):
return self.nodeset_.copy()
@property
def timeset(self):
return self.timeset_.copy()
@property
def linkset(self):
return self.temporal_linkset_.linkset
@property
def temporal_nodeset(self):
return self.temporal_nodeset_.copy()
@property
def temporal_linkset(self):
return self.temporal_linkset_.copy()
@property
def empty(self):
return not bool(self)
def graph_at(self, t=None):
from .graph import Graph
if t is None:
def fun(nodes, links):
return Graph(nodes, links)
return self.temporal_nodeset_.nodes_at(t).merge(
self.temporal_linkset_.links_at(t), fun)
else:
return Graph(self.temporal_nodeset_.nodes_at(t),
self.temporal_linkset_.links_at(t))
@property
def density(self):
"""Calculate the density of the temporal-link-set.
Parameters
----------
None. Property.
Returns
-------
ns_coverage : Real
Returns :math:`\\delta(S) = \\frac{|E|}{\sum_{uv \\in V\\times V}|T_{u} \\cap T_{v}|}`
"""
denom = float(self.temporal_nodeset_.total_common_time)
if denom > .0:
return self.temporal_linkset_.size / denom
else:
return .0
@property
def weighted_density(self):
"""Calculate the weighted density of the temporal-link-set.
Parameters
----------
None. Property.
Returns
-------
ns_coverage : Real
Returns :math:`\\delta_{w}(S)\\frac{|E_{w}|}{\sum_{uv \\in V\\times V}|T_{u} \\cap T_{v}|}`
"""
denom = float(self.temporal_nodeset_.total_common_time)
if denom > .0:
return self.temporal_linkset_.weighted_size / denom
else:
return .0
@property
def coverage(self):
"""Calculate the coverage of the stream-graph.
Parameters
----------
None. Property.
Returns
-------
ns_coverage : Real
Returns :math:`c(S)=\\frac{|W|}{|V\\times T|}`
"""
denom = float(self.timeset_.size * self.nodeset_.size)
if denom > .0:
return self.temporal_nodeset_.size / denom
else:
return .0
def node_contribution_of(self, u=None):
"""Calculate the contibution of a node inside the stream_graph.
Parameters
----------
u: NodeId or None
Returns
-------
time_coverage_node : Real or NodeCollection(Real)
Returns :math:`n_{u}=\\frac{|T_{u}|}{|T|}`.
If u is None, returns a dictionary of all nodes and their coverages.
"""
denom = float(self.timeset_.size)
if u is None:
def fun(x, y):
if denom != .0:
return y / denom
else:
return .0
return self.temporal_nodeset_.duration_of().map(fun)
else:
if denom == .0:
return .0
else:
return self.temporal_nodeset_.duration_of(u) / denom
def node_contribution_at(self, t=None):
"""Calculate the node contribution at a time instant inside the stream_graph.
Parameters
----------
t: time or None
Returns
-------
node_coverage : Real or TimeCollection
Returns :math:`k_{t}=\\frac{|V_{t}|}{|V|}`.
If None returns the time coverage for each node at each time-event.
"""
denom = float(self.nodeset_.size)
if t is None:
if denom > .0:
def fun(t, v):
return v / denom
return self.temporal_nodeset_.n_at().map(fun)
else:
return TimeCollection(
instants=self.temporal_nodeset_.instantaneous)
else:
if denom > .0:
return self.temporal_nodeset_.n_at(t) / denom
else:
return .0
def link_contribution_at(self, t=None):
"""Calculate the contribution of a link inside the stream_graph.
Parameters
----------
t: time or None
Returns
-------
node_coverage : Real or TimeCollection
Returns :math:`l_{t}=\\frac{|E_{t}|}{|V*(V-1)|}`.
If None returns the time coverage for each node at each time-event.
"""
denom = float(self.nodeset_.size)
denom = denom * (denom - 1)
if t is None:
if denom > .0:
def fun(t, v):
return v / denom
return self.temporal_linkset_.m_at().map(fun)
else:
return TimeCollection(
instants=self.temporal_linkset_.instantaneous)
else:
if denom > .0:
return self.temporal_linkset_.m_at(t) / denom
else:
return .0
def link_density_of(self, l=None, weights=False, direction='out'):
"""Calculate the density of a link inside the stream_graph.
Parameters
----------
l: (NodeId, NodeId) or None
direction: 'in', 'out' or 'both', default='out'
Returns
-------
time_coverage : Real or LinkCollection
Returns :math:`\\frac{|T_{uv}|}{|T_{u} \\cap T_{v}|}`.
If l is None, returns a dictionary of all links and their coverages.
"""
if l is None:
times = self.temporal_linkset_.duration_of(direction=direction,
weights=weights)
active_links = set(k for k, v in times if v > .0)
common_times = self.temporal_nodeset_.common_time_pair(
l=active_links)
def fun(k, v):
return (times[k] / float(v) if v > .0 else .0)
return common_times.map(fun)
else:
denom = float(self.temporal_nodeset_.common_time_pair(l))
if denom == .0:
return .0
else:
return self.temporal_linkset_.duration_of(
l, direction=direction, weights=weights) / denom
def density_at(self, t=None, weights=False):
"""Calculate the density at a time instant inside the stream_graph.
Parameters
----------
t: time or None
Returns
-------
link_coverage : Real or TimeCollection
Returns :math:`l_{t}=\\frac{|E_{t}|}{V(t)*(V(t)-1)}`.
Returns the time collection for each link at each time-event.
"""
if t is None:
ns, ms = self.temporal_nodeset_.n_at(
t=None), self.temporal_linkset_.m_at(t=None, weights=weights)
def fun(x, y):
denom = float(x * (x - 1))
return ((y / denom) if denom != .0 else .0)
return ns.merge(ms, fun, missing_value=.0)
else:
denom = float(self.temporal_nodeset_.n_at(t))
denom = denom * (denom - 1)
if denom > .0:
return self.temporal_linkset_.m_at(t, weights=weights) / denom
else:
return .0
def node_density_of(self, u=None, direction='out', weights=False):
"""Calculate the node density of a node inside the stream_graph.
Parameters
----------
u: NodeId or None
direction: 'in', 'out' or 'both', default='out'
weigths: bool, default=False
Returns
-------
neighbor_coverage : Real or dict
Returns :math:`\\frac{\\sum_{u\\in V, u\\neq v}|T_{uv}|}{\\sum_{v\\in V, v\\neq u}{|T_{u}\\cap T_{v}|}}`
If u is None, returns a dictionary of all nodes and their neighbor coverages.
"""
direction = ('in' if direction == 'out' else
('out' if direction == 'in' else direction))
if u is None:
m = self.temporal_linkset_.degree_of(direction=direction,
weights=weights)
active_nodes = set(k for k, v in m if v > .0)
common_times = dict(
self.temporal_nodeset_.common_time(u=active_nodes))
# maybe add a u = nodes argument in temporal_nodeset_common_times
def fun(x, y):
ct = common_times.get(x, .0)
if y > .0 and ct > .0:
return y / float(ct)
return y / common_times[x]
return m.map(fun)
else:
denom = float(self.temporal_nodeset_.common_time(u))
if denom == .0:
return .0
else:
return self.temporal_linkset_.degree_of(
u, direction, weights=weights) / denom
def neighbor_coverage_at(self,
u=None,
t=None,
direction='out',
weights=False):
"""Calculate the coverage of a node inside the stream_graph.
Parameters
----------
u: NodeId or None
t: Time or None
direction: 'in', 'out' or 'both', default='out'
Returns
-------
time_coverage : Real
Returns :math:`\\frac{|N_{t}(u)|}{|V(t)|}`.
If u is None return the neighbor coverage for each node at time t.
If t is None return the neighbor coverage for node u for all time-events.
If u and t are None return the neighbor coverage for each node at each time-event.
"""
def coverage(x, y):
return (x / float(y) if y != .0 else .0)
if t is None:
n = self.temporal_nodeset_.n_at(None)
if u is None:
neighbors = self.temporal_linkset_.degree_at(
None, None, direction, weights)
def fun(x, y):
return y.merge(n, coverage)
return neighbors.map(fun)
else:
return self.temporal_linkset_.degree_at(
u, None, direction, weights).merge(n, coverage)
else:
denom = float(self.temporal_nodeset_.n_at(t))
if u is None:
def fun(x, y):
return coverage(y, denom)
return self.temporal_linkset_.degree_at(
None, t, direction, weights).map(fun)
else:
if denom > .0:
return self.temporal_linkset_.degree_at(
u, t, direction, weights) / denom
else:
return .0
def mean_degree_at(self, t=None, weights=False):
"""Calculate the mean degree at a give time.
Parameters
----------
t: Time or None
Returns
-------
time_coverage : Real or TimeCollection
Returns :math:`\\frac{|E_{t}|}{|V_{t}|}`
Returns mean degree at each time t.
"""
if t is None:
if bool(self):
ns, ms = self.temporal_nodeset_.n_at(
t=None), self.temporal_linkset_.m_at(t=None,
weights=weights)
def fun(x=.0, y=.0):
return (y / x if x != .0 and y != .0 else .0)
return ns.merge(ms, fun, missing_value=.0)
return list()
else:
denom = float(self.temporal_nodeset_.n_at(t))
if denom > .0:
return self.temporal_linkset_.m_at(t, weights=weights) / denom
else:
return .0
def __and__(self, sg):
if isinstance(sg, StreamGraph):
return StreamGraph(self.nodeset_ & sg.nodeset_,
self.timeset_ & sg.timeset_,
self.temporal_nodeset_ & sg.temporal_nodeset_,
self.temporal_linkset_ & sg.temporal_linkset_)
else:
raise UnrecognizedStreamGraph('right operand')
def __or__(self, sg):
if isinstance(sg, StreamGraph):
return StreamGraph(self.nodeset_ | sg.nodeset_,
self.timeset_ | sg.timeset_,
self.temporal_nodeset_ | sg.temporal_nodeset_,
self.temporal_linkset_ | sg.temporal_linkset_)
else:
raise UnrecognizedStreamGraph('right operand')
def __sub__(self, sg):
if isinstance(sg, StreamGraph):
nsm = self.temporal_nodeset_ - sg.temporal_nodeset_
return StreamGraph((self.nodeset_ - sg.nodeset_) | nsm.nodeset,
self.timeset_ - sg.timeset_, nsm,
self.temporal_linkset_ - sg.temporal_linkset_)
else:
raise UnrecognizedStreamGraph('right operand')
def issuperset(self, sg):
if isinstance(sg, StreamGraph):
return (self.nodeset_.issuperset(sg.nodeset_)
and self.timeset_.issuperset(sg.timeset_)
and self.temporal_nodeset_.issuperset(sg.temporal_nodeset_)
and self.temporal_linkset_.issuperset(
sg.temporal_linkset_))
else:
raise UnrecognizedStreamGraph('right operand')
return False
@property
def n(self):
"""Calculate the number of nodes of the stream-graph.
Parameters
----------
None. Property.
Returns
-------
n : Real
Returns :math:`\\frac{|W|}{|T|}`
"""
denom = float(self.timeset_.size)
if denom > .0:
return self.temporal_nodeset_.size / denom
else:
return .0
@property
def m(self):
"""Calculate the number of edges of the stream-graph.
Parameters
----------
None. Property.
Returns
-------
n : Real
Returns :math:`\\frac{|E|}{|T|}`
"""
denom = float(self.timeset_.size)
if denom > .0:
return self.temporal_linkset_.size / denom
else:
return .0
def induced_substream(self, tns):
"""Calculate the induced substream of the stream-graph from a TemporalNodeSet.
Parameters
----------
tns: TemporalNodeSet
Returns
-------
stream_graph : StreamGrpah
Returns the induced substream.
"""
assert isinstance(tns, ABC.TemporalNodeSet)
tns_is = self.temporal_nodeset_ & tns
tls_ind = self.temporal_linkset_.induced_substream(tns_is)
return StreamGraph(self.nodeset, self.timeset, tns_is, tls_ind)
def substream(self, nsu=None, nsv=None, ts=None):
if nsu is not None:
if not isinstance(nsu, ABC.NodeSet):
try:
nsu = NodeSetS(nsu)
except Exception as ex:
raise UnrecognizedNodeSet('nsu: ' + str(ex))
if nsv is not None:
if not isinstance(nsv, ABC.NodeSet):
try:
nsv = NodeSetS(nsv)
except Exception as ex:
raise UnrecognizedNodeSet('nsv: ' + str(ex))
if ts is not None:
if not isinstance(ts, ABC.TimeSet):
try:
ts = list(ts)
if any(isinstance(t, Iterable) for t in ts):
from stream_graph import TimeSetDF
ts = TimeSetDF(ts, discrete=self.timeset_.discrete)
else:
from stream_graph import ITimeSetS
ts = ITimeSetS(ts, discrete=self.timeset_.discrete)
except Exception as ex:
raise UnrecognizedTimeSet('ts: ' + ex)
if all(o is None for o in [nsu, nsv, ts]):
return self.copy()
if nsu is not None and nsv is not None:
ns = nsu | nsv
elif nsu is not None:
ns = nsu
elif nsv is not None:
ns = nsv
else:
ns = None
# Build the new nodeset
nodeset = (self.nodeset if ns is None else self.nodeset_ & ns)
# Build the new timeset
timeset = (self.timeset if ts is None else self.timeset_ & ts)
# Build the new temporal-nodeset
temporal_nodeset = self.temporal_nodeset_.substream(nsu=ns, ts=ts)
# Build the new temporal-nodeset
temporal_linkset = self.temporal_linkset_.substream(nsu=nsu,
nsv=nsv,
ts=ts)
return self.__class__(nodeset, timeset, temporal_nodeset,
temporal_linkset)
def discretize(self, bins=None, bin_size=None):
"""Returns a discrete version of the current TemporalLinkSet.
Parameters
----------
bins : Iterable or None.
If None, step should be provided.
If Iterable it should contain n+1 elements that declare the start and the end of all (continuous) bins.
bin_size : Int or datetime
If bins is provided this argument is ommited.
Else declare the size of each bin.
Returns
-------
timeset_discrete : TimeSet
Returns a discrete version of the TimeSet.
bins : list
A list of the created bins.
"""
if self.discrete:
timeset, bins = self.timeset_.discretize(bins, bin_size)
tns, _ = self.temporal_nodeset_.discretize(bins=bins)
tls, _ = self.temporal_linkset_.discretize(bins=bins)
return self.__class__(self.nodeset, timeset, tns, tls), bins
else:
warn('Stream-Graph is already discrete')
return self
@property
def aggregated_graph(self):
from stream_graph import Graph
return Graph(self.nodeset_, self.linkset)
@property
def data_cube(self):
if not bool(self):
return DataCube()
if self.discrete:
column_sizes = {
('u', ): self.nodeset_.size,
('v', ): self.nodeset_.size,
('ts', ): self.timeset_.size,
('u', 'ts'): self.temporal_nodeset_.size,
('v', 'ts'): self.temporal_nodeset_.size
}
if not isinstance(self.temporal_linkset_, ABC.ITemporalLinkSet):
iter_ = iter((u, v, t)
for (u, v, ts, tf) in self.temporal_linkset_
for t in range(ts, tf + 1))
else:
iter_ = iter(self.temporal_linkset_)
return DataCube(iter_,
columns=['u', 'v', 'ts'],
column_sizes=column_sizes)
else:
raise ValueError(
'Stream-Graph should be discrete to be convertible to a data-cube'
)
"""
==========================================
Initialize and print a simple stream-graph
==========================================
An example plot of a :class:`stream_graph.StreamGraph` made from a combination of :class:`stream_graph.NodeSetS`, :class:`stream_graph.TimeSetDF` , :class:`stream_graph.TemporalNodeSetDF`, :class:`stream_graph.LinkStreamDF`:
.. image:: images/sphx_glr_plot_intro_001.png
:align: center
using the :class:`stream_graph.Visualizer`.
"""
from __future__ import print_function
print(__doc__)
from stream_graph import Visualizer, TemporalLinkSetDF, NodeSetS, TemporalNodeSetDF, TimeSetDF, StreamGraph
nodeset = NodeSetS({1, 2, 3})
timeset = TimeSetDF([(0, 10)])
temporal_nodeset = TemporalNodeSetDF([(1, 1, 9), (2, 2, 5), (2, 7, 9)])
temporal_linkset = TemporalLinkSetDF([(1, 2, 2, 4), (1, 2, 3, 5),
(1, 2, 6, 8)],
disjoint_intervals=False)
stream_graph = StreamGraph(nodeset, timeset, temporal_nodeset,
temporal_linkset)
# Visualize svg
Visualizer().fit(stream_graph).save(
'../doc/auto_examples/images/sphx_glr_plot_intro_001.png', 'png')
def test_time_set_df():
for d, do in [(False, 0), (True, 2)]:
ita, itb, itc = ((tuple(), tuple(), tuple()) if d else
(('both', ), ('right', ), ('left', )))
times_a = [(1, 3), (5, 7), (6, 10)]
tsa = TimeSetDF(times_a, disjoint_intervals=False, discrete=d)
assert bool(times_a)
assert not bool(TimeSetDF({}))
assert not bool(TimeSetDF())
assert_equal(list(tsa), [(1, 3) + ita, (5, 10) + ita])
assert_equal(tsa.size, 7 + do)
assert_equal(TimeSetDF().size, 0)
assert 2 in tsa
assert 1.5, 2.3 in tsa
assert 2.4, 5.2 not in tsa
assert 4 not in tsa
assert (5, 10) in tsa
times_b = [(2, 4), (6, 11)]
tsb = TimeSetDF(times_b, discrete=d)
assert_equal(list(tsb & tsa), list(tsa & tsb))
assert_equal((tsb & tsa).size, 5 + do)
assert_equal(list((tsb & tsa)), sorted([(2, 3) + ita, (6, 10) + ita]))
assert_equal(list(tsb | tsa), list(tsa | tsb))
assert_equal((tsb | tsa).size, 9 + do)
assert_equal(
list((tsb | tsa)),
sorted([(1, 11)] if d else [(1, 4, 'both'), (5, 11, 'both')]))
assert_equal(
list(tsa - tsb),
sorted([(1, 1), (5, 5)] if d else [(1, 2, 'left'), (5, 6,
'left')]))
assert_equal((tsa - tsb).size, 2)
assert_equal(
list(tsb - tsa),
sorted([(4, 4), (11, 11)] if d else [(3, 4,
'right'), (10, 11,
'right')]))
assert_equal((tsb - tsa).size, 2)
# assert tsa.issuperset(tsa & tsb)
# assert tsb.issuperset(tsa & tsb)
# assert (tsa | tsb).issuperset(tsa)
# assert (tsa | tsb).issuperset(tsb)
# assert tsa.issuperset(tsa - tsb)
try:
tsa | 1
except UnrecognizedTimeSet:
pass
try:
tsa & 1
except UnrecognizedTimeSet:
pass
try:
tsa - 1
except UnrecognizedTimeSet:
pass
def test_temporal_link_set_df():
assert not bool(TemporalLinkSetDF([]))
assert not bool(TemporalLinkSetDF())
assert_equal(TemporalLinkSetDF().size, 0)
assert_equal(TemporalLinkSetDF([]).size, 0)
assert TemporalLinkSetDF().discrete
for d in [False, True]:
df = [(1, 2, 2, 3), (1, 2, 3, 5), (2, 1, 6, 8), (2, 1, 1, 3)]
lsa = TemporalLinkSetDF(df, disjoint_intervals=False, discrete=d)
assert bool(lsa)
if d:
assert_equal(set(lsa), {(1, 2, 2, 5), (2, 1, 6, 8), (2, 1, 1, 3)})
else:
assert_equal(set(lsa), {(1, 2, 2, 5, 'both'), (2, 1, 6, 8, 'both'),
(2, 1, 1, 3, 'both')})
assert_equal(lsa.m, 2)
assert_equal(lsa.size, 7 + 3 * int(d))
assert (1, 2, None) in lsa
assert (1, None, 3.4) in lsa
assert (5, None, None) not in lsa
assert (1, 3, 3.4) not in lsa
assert (1, 2, (3.4, 4)) in lsa
assert_equal(lsa.duration_of((1, 2)), 3 + int(d))
assert_equal(lsa.duration_of((2, 1)), 4 + 2 * int(d))
assert_equal(lsa.duration_of((2, 1)),
lsa.duration_of((1, 2), direction='in'))
assert_equal(lsa.duration_of((1, 2), direction='both'), 6 + 2 * int(d))
assert_equal(lsa.duration_of((5, 1)), 0)
assert_equal(dict(lsa.duration_of()), {
(1, 2): 3.0 + int(d),
(2, 1): 4.0 + 2 * int(d)
})
assert_equal(set(lsa.links_at(1)), {(2, 1)})
assert_equal(set(lsa.links_at((2, 2.5))), {(1, 2), (2, 1)})
assert_equal(set(lsa.links_at(10)), set())
tg = lsa.links_at()
assert isinstance(tg, TimeGenerator)
if d:
assert_equal([(t, set(ns)) for t, ns in tg], [(1, {(2, 1)}),
(2, {(1, 2),
(2, 1)}),
(4, {(1, 2)}),
(6, {(2, 1)}),
(9, set())])
else:
assert_equal([(t, set(ns)) for t, ns in tg],
[((1, True), {(2, 1)}), ((2, True), {(1, 2), (2, 1)}),
((3, False), {(1, 2)}), ((5, False), set()),
((6, True), {(2, 1)}), ((8, False), set())])
assert not tg.instants
assert not len([a for a in tg])
if d:
assert_equal(set(lsa.times_of((1, 2))), {(2, 5)})
assert_equal({t: list(ns)
for t, ns in lsa.times_of()}, {
(1, 2): [(2, 5)],
(2, 1): [(1, 3), (6, 8)]
})
else:
assert_equal(set(lsa.times_of((1, 2))), {(2, 5, 'both')})
assert_equal({t: list(ns)
for t, ns in lsa.times_of()}, {
(1, 2): [(2, 5, 'both')],
(2, 1): [(1, 3, 'both'), (6, 8, 'both')]
})
assert_equal(set(lsa.times_of((10, 3))), set())
assert_equal(lsa.m_at(2), 2)
assert_equal(lsa.m_at(10), 0)
df = [(1, 2, 1, 4), (1, 2, 6, 7),
((2, 1, 3, 5) if d else (2, 1, 2.5, 2.6))]
lsb = TemporalLinkSetDF(df, discrete=d)
assert_equal(set(lsb & lsa), set(lsa & lsb))
assert isinstance(lsa & lsb, TemporalLinkSetDF)
if d:
assert_equal(set(lsa & lsb), {(1, 2, 2, 4), (2, 1, 3, 3)})
else:
assert_equal(set(lsa & lsb), {(2, 1, 2.5, 2.6, 'both'),
(1, 2, 2.0, 4.0, 'both')})
assert_equal((lsa & lsb).size, (4 if d else 2.1))
# incorrect for the discrete case as the intervals are not discrete
assert isinstance(lsa | lsb, TemporalLinkSetDF)
if d:
assert_equal(set((lsb | lsa)), {(2, 1, 1, 8), (1, 2, 1, 7)})
else:
assert_equal(
set((lsb | lsa)), {(2, 1, 1, 3, 'both'), (2, 1, 6, 8, 'both'),
(1, 2, 1, 5, 'both'), (1, 2, 6, 7, 'both')})
assert_equal((lsb | lsa).size, 9 + 6 * int(d))
assert_equal(set(lsb | lsa), set(lsa | lsb))
if d:
assert_equal(set(lsb - lsa), {(2, 1, 4, 5), (1, 2, 1, 1),
(1, 2, 6, 7)})
else:
assert_equal(set(lsb - lsa), {(1, 2, 1, 2, 'left'),
(1, 2, 6, 7, 'both')})
assert_equal((lsb - lsa).size, 2 + 3 * int(d))
assert isinstance(lsb - lsa, TemporalLinkSetDF)
if d:
assert_equal(set(lsa - lsb), {(2, 1, 1, 2), (2, 1, 6, 8),
(1, 2, 5, 5)})
else:
assert_equal(
set(lsa - lsb), {(1, 2, 4.0, 5.0, 'right'),
(2, 1, 1.0, 2.5, 'left'),
(2, 1, 6.0, 8.0, 'both'),
(2, 1, 2.6, 3.0, 'right')})
assert isinstance(lsa - lsb, TemporalLinkSetDF)
assert_equal((lsa - lsb).size, 4.9 + 1.1 * int(d))
assert lsa.issuperset(lsa & lsb)
assert lsb.issuperset(lsa & lsb)
assert (lsa | lsb).issuperset(lsa)
assert (lsa | lsb).issuperset(lsb)
assert lsa.issuperset(lsa - lsb)
if d:
assert_equal(set(lsa.neighbors_of(1)), {(2, 2, 5)})
assert_equal(set(lsa.neighbors_of(1, 'in')), {(2, 6, 8),
(2, 1, 3)})
assert_equal(set(lsa.neighbors_of(1, 'both')), {(2, 1, 8)})
assert_equal(
{n: list(ns)
for n, ns in lsa.neighbors_of(direction='out')}, {
1: [(2, 2, 5)],
2: [(1, 1, 3), (1, 6, 8)]
})
assert_equal(
{n: list(ns)
for n, ns in lsa.neighbors_of(direction='in')}, {
1: [(2, 1, 3), (2, 6, 8)],
2: [(1, 2, 5)]
})
assert_equal(
{n: list(ns)
for n, ns in lsa.neighbors_of(direction='both')}, {
1: [(2, 1, 8)],
2: [(1, 1, 8)]
})
else:
assert_equal(set(lsa.neighbors_of(1)), {(2, 2, 5, 'both')})
assert_equal(set(lsa.neighbors_of(1, 'in')), {(2, 6, 8, 'both'),
(2, 1, 3, 'both')})
assert_equal(set(lsa.neighbors_of(1, 'both')), {(2, 6, 8, 'both'),
(2, 1, 5, 'both')})
assert_equal(
{n: list(ns)
for n, ns in lsa.neighbors_of(direction='out')}, {
1: [(2, 2, 5, 'both')],
2: [(1, 1, 3, 'both'), (1, 6, 8, 'both')]
})
assert_equal(
{n: list(ns)
for n, ns in lsa.neighbors_of(direction='in')}, {
2: [(1, 2, 5, 'both')],
1: [(2, 1, 3, 'both'), (2, 6, 8, 'both')]
})
assert_equal(
{n: list(ns)
for n, ns in lsa.neighbors_of(direction='both')}, {
2: [(1, 1, 5, 'both'), (1, 6, 8, 'both')],
1: [(2, 1, 5, 'both'), (2, 6, 8, 'both')]
})
na = lsa.neighbors_at(1)
assert isinstance(na, TimeGenerator)
if d:
assert_equal([(t, set(ns)) for t, ns in na], [(2, {2}),
(6, set())])
else:
assert_equal([(t, set(ns)) for t, ns in na], [((2, True), {2}),
((5, False), set())])
assert not tg.instants
assert not len([a for a in na])
assert_equal(set(lsa.neighbors_at(1, 2)), {2})
assert_equal(
{i: set(nsa)
for i, nsa in lsa.neighbors_at(None, 2, 'out')}, {
1: {2},
2: {1}
})
if d:
assert_equal(
{
i: [(t, set(ts)) for t, ts in nsa]
for i, nsa in lsa.neighbors_at()
}, {
1: [(2, {2}), (6, set())],
2: [(1, {1}), (4, set()), (6, {1}), (9, set())]
})
else:
assert_equal(
{
i: [(t, set(ts)) for t, ts in nsa]
for i, nsa in lsa.neighbors_at()
}, {
1: [((2, True), {2}), ((5, False), set())],
2: [((1, True), {1}), ((3, False), set()),
((6, True), {1}), ((8, False), set())]
})
na = lsa.degree_at(1)
assert isinstance(na, TimeCollection)
if d:
assert_equal([(t, ns) for t, ns in na], [(2, 1), (6, 0)])
else:
assert_equal([(t, ns) for t, ns in na], [((2, True), 1),
((5, False), 0)])
assert not na.instants
assert len([a for a in na])
assert_equal(lsa.degree_at(1, 2), 1)
assert_equal({i: n
for i, n in lsa.degree_at(None, 2, 'out')}, {
1: 1,
2: 1
})
if d:
assert_equal(
{i: [(t, ts) for t, ts in nsa]
for i, nsa in lsa.degree_at()}, {
1: [(2, 1), (6, 0)],
2: [(1, 1), (4, 0), (6, 1), (9, 0)]
})
else:
assert_equal(
{i: [(t, ts) for t, ts in nsa]
for i, nsa in lsa.degree_at()}, {
2: [((1, True), 1), ((3, False), 0), ((6, True), 1),
((8, False), 0)],
1: [((2, True), 1), ((5, False), 0)]
})
nsa = NodeSetS({1})
nsb = NodeSetS({2})
ts = TimeSetDF([(1, 4)], discrete=d)
if d:
assert_equal(set(lsa.substream(nsa, nsb, ts)), {(1, 2, 2, 4)})
else:
assert_equal(set(lsa.substream(nsa, nsb, ts)),
{(1, 2, 2, 4, 'both')})
nsmb = TemporalNodeSetDF([(1, 1, 4), (2, 1, 3)], discrete=d)
assert_equal(
set(lsa.induced_substream(nsmb)), {
a + (tuple() if d else ('both', ))
for a in [(1, 2, 2, 3), (2, 1, 1, 3)]
})
nsma = TemporalNodeSetDF([(1, 1, 4)], discrete=d)
assert_equal(set(lsa.temporal_neighborhood(nsma, direction='out')),
{(2, 2, 4)} if d else {(2, 2, 4, 'both')})
try:
lsa | 1
except UnrecognizedTemporalLinkSet:
pass
try:
lsa & 1
except UnrecognizedTemporalLinkSet:
pass
try:
lsa - 1
except UnrecognizedTemporalLinkSet:
pass
df = [(1, 2, 2, 10), (2, 3, 4, 16), (1, 3, 6, 12), (3, 4, 8, 16),
(2, 4, 13, 17)]
assert_equal(
TemporalLinkSetDF(df, discrete=d).get_maximal_cliques(),
{(frozenset({2, 4}), (13, 17)), (frozenset({3, 4}), (8, 16)),
(frozenset({2, 3, 4}), (13, 16)), (frozenset({2, 3}), (4, 16)),
(frozenset({1, 2, 3}), (6, 10)), (frozenset({1, 3}), (6, 12)),
(frozenset({1, 2}), (2, 10))})
# weighted
df = [(1, 2, 2, 3, 1), (1, 2, 3, 5, 1), (2, 1, 6, 8, 1),
(2, 1, 1, 3, 1)]
lsa = TemporalLinkSetDF(df,
disjoint_intervals=False,
weighted=True,
discrete=d)
assert bool(lsa)
if d:
assert_equal(
set(lsa), {(1, 2, 4, 5, 1), (2, 1, 1, 3, 1), (2, 1, 6, 8, 1),
(1, 2, 3, 3, 2), (1, 2, 2, 2, 1)})
else:
assert_equal(
set(lsa), {(2, 1, 6, 8, 'left', 1), (2, 1, 1, 3, 'left', 1),
(1, 2, 2, 5, 'left', 1)})
assert_equal(lsa.m, 2)
assert_equal(lsa.size, 7 + 3 * int(d))
assert (1, 2, None) in lsa
assert (5, None, None) not in lsa
if d:
assert (1, None, 3) in lsa
assert (1, 3, 3) not in lsa
assert (1, 2, (2, 3)) in lsa
else:
assert (1, None, 3.4) in lsa
assert (1, 3, 3.4) not in lsa
assert (1, 2, (3.4, 4)) in lsa
assert_equal(lsa.duration_of((1, 2)), 3 + int(d))
assert_equal(lsa.duration_of((2, 1)), 4 + 2 * int(d))
assert_equal(lsa.duration_of((2, 1)),
lsa.duration_of((1, 2), direction='in'))
assert_equal(lsa.duration_of((1, 2), direction='both'), 6 + 2 * int(d))
assert_equal(lsa.duration_of((5, 1)), 0)
assert_equal(dict(lsa.duration_of()), {
(1, 2): 3 + int(d),
(2, 1): 4.0 + 2 * int(d)
})
assert_equal(set(lsa.links_at(1)), {(2, 1, 1)})
if d:
assert_equal(set(lsa.links_at((2, 3))), {(1, 2, 3), (2, 1, 1)})
else:
assert_equal(set(lsa.links_at((2, 2.5))), {(1, 2, 1), (2, 1, 1)})
assert_equal(set(lsa.links_at(10)), set())
tg = lsa.links_at()
assert isinstance(tg, TimeGenerator)
if d:
assert_equal([(t, set(ns)) for t, ns in tg], [(1, {(2, 1, 1)}),
(2, {(1, 2, 1),
(2, 1, 1)}),
(3, {(1, 2, 2),
(2, 1, 1)}),
(4, {(1, 2, 1)}),
(6, {(2, 1, 1)}),
(9, set())])
else:
assert_equal([(t, set(ns)) for t, ns in tg],
[((1, True), {(2, 1, 1)}),
((2, True), {(1, 2, 1), (2, 1, 1)}),
((3, True), {(1, 2, 1)}), ((5, True), set()),
((6, True), {(2, 1, 1)}), ((8, True), set())])
assert not tg.instants
assert not len([a for a in tg])
if d:
assert_equal(set(lsa.times_of((1, 2))), {(2, 5)})
assert_equal({t: list(ns)
for t, ns in lsa.times_of()}, {
(1, 2): [(2, 5)],
(2, 1): [(1, 3), (6, 8)]
})
else:
assert_equal(set(lsa.times_of((1, 2))), {(2, 5, 'left')})
assert_equal({t: list(ns)
for t, ns in lsa.times_of()}, {
(1, 2): [(2, 5, 'left')],
(2, 1): [(1, 3, 'left'), (6, 8, 'left')]
})
assert_equal(set(lsa.times_of((10, 3))), set())
assert_equal(lsa.m_at(2), 2)
assert_equal(lsa.m_at(10), 0)
df = ([(1, 2, 1, 4, 1), (1, 2, 6, 7, 1),
(2, 1, 2, 3, 1)] if d else [(1, 2, 1, 4, 1), (1, 2, 6, 7, 1),
(2, 1, 2.5, 2.6, 1)])
lsb = TemporalLinkSetDF(df, weighted=True, discrete=d)
assert_equal(set(lsb & lsa), set(lsa & lsb))
assert isinstance(lsa & lsb, TemporalLinkSetDF)
if d:
assert_equal(set(lsa & lsb), {(2, 1, 2, 3, 1), (1, 2, 2, 4, 1)})
else:
assert_equal(set(lsa & lsb), {(2, 1, 2.5, 2.6, 'left', 1),
(1, 2, 2.0, 4.0, 'left', 1)})
assert_equal((lsa & lsb).size, 2.1 + 2.9 * int(d))
assert isinstance(lsa | lsb, TemporalLinkSetDF)
if d:
assert_equal(
set((lsb | lsa)), {(2, 1, 1, 1, 1), (1, 2, 2, 2, 2),
(1, 2, 5, 7, 1), (2, 1, 2, 3, 2),
(1, 2, 3, 4, 3), (2, 1, 4, 5, 1),
(1, 2, 1, 1, 1), (2, 1, 6, 8, 2)})
else:
assert_equal(
set((lsb | lsa)), {(1, 2, 4.0, 5.0, 'left', 1),
(2, 1, 2.6, 3.0, 'left', 1),
(2, 1, 6.0, 8.0, 'left', 1),
(1, 2, 2.0, 4.0, 'left', 2),
(2, 1, 2.5, 2.6, 'left', 2),
(1, 2, 6.0, 7.0, 'left', 1),
(1, 2, 1.0, 2.0, 'left', 1),
(2, 1, 1.0, 2.5, 'left', 1)})
assert_equal((lsb | lsa).size, 6 * int(d) + 9)
assert_equal(set(lsb | lsa), set(lsa | lsb))
if d:
assert_equal(set(lsb - lsa), {(1, 2, 6, 7, 1), (1, 2, 1, 1, 1)})
else:
assert_equal(set(lsb - lsa), {(1, 2, 6.0, 7.0, 'left', 1.0),
(1, 2, 1.0, 2.0, 'left', 1.0)})
assert_equal((lsb - lsa).size, 2 + int(d))
assert isinstance(lsb - lsa, TemporalLinkSetDF)
if d:
assert_equal(set(lsa - lsb), {(2, 1, 1, 1, 1), (2, 1, 6, 8, 1),
(1, 2, 5, 5, 1), (1, 2, 3, 3, 1)})
else:
assert_equal(
set(lsa - lsb), {(1, 2, 4.0, 5.0, 'left', 1.0),
(2, 1, 1.0, 2.5, 'left', 1.0),
(2, 1, 6.0, 8.0, 'left', 1.0),
(2, 1, 2.6, 3.0, 'left', 1.0)})
assert isinstance(lsa - lsb, TemporalLinkSetDF)
assert_equal((lsa - lsb).size, 4.9 + 1.1 * int(d))
assert lsa.issuperset(lsa & lsb)
assert lsb.issuperset(lsa & lsb)
assert (lsa | lsb).issuperset(lsa)
assert (lsa | lsb).issuperset(lsb)
assert lsa.issuperset(lsa - lsb)
if d:
assert_equal(set(lsa.neighbors_of(1)), {(2, 2, 5)})
assert_equal(set(lsa.neighbors_of(1, direction='in')), {(2, 6, 8),
(2, 1, 3)})
assert_equal(set(lsa.neighbors_of(1, direction='both')),
{(2, 1, 8)})
assert_equal({n: list(ns)
for n, ns in lsa.neighbors_of()}, {
1: [(2, 2, 5)],
2: [(1, 1, 3), (1, 6, 8)]
})
else:
assert_equal(set(lsa.neighbors_of(1)), {(2, 2, 5, 'left')})
assert_equal(set(lsa.neighbors_of(1, direction='in')),
{(2, 6, 8, 'left'), (2, 1, 3, 'left')})
assert_equal(set(lsa.neighbors_of(1, direction='both')),
{(2, 6, 8, 'left'), (2, 1, 5, 'left')})
assert_equal({n: list(ns)
for n, ns in lsa.neighbors_of()}, {
1: [(2, 2, 5, 'left')],
2: [(1, 1, 3, 'left'), (1, 6, 8, 'left')]
})
na = lsa.neighbors_at(1)
assert isinstance(na, TimeGenerator)
if d:
assert_equal([(t, set(ns)) for t, ns in na], [(2, {2}),
(6, set())])
else:
assert_equal([(t, set(ns)) for t, ns in na], [((2, True), {2}),
((5, True), set())])
assert not tg.instants
assert not len([a for a in na])
assert_equal(set(lsa.neighbors_at(1, 2)), {2})
assert_equal(
{i: set(nsa)
for i, nsa in lsa.neighbors_at(None, 2, 'out')}, {
1: {2},
2: {1}
})
if d:
assert_equal(
{
i: [(t, set(ts)) for t, ts in nsa]
for i, nsa in lsa.neighbors_at()
}, {
1: [(2, {2}), (3, set()), (3, {2}), (4, set()), (4, {2}),
(6, set())],
2: [(1, {1}), (4, set()), (6, {1}), (9, set())]
})
else:
assert_equal(
{
i: [(t, set(ts)) for t, ts in nsa]
for i, nsa in lsa.neighbors_at()
}, {
1: [((2, True), {2}), ((5, True), set())],
2: [((1, True), {1}), ((3, True), set()), ((6, True), {1}),
((8, True), set())]
})
for w in [False, True]:
if d:
if w:
assert_equal(
{t: list(tc)
for t, tc in lsa.degree_at(weights=w)}, {
1: [(2, 1), (3, 2), (4, 1), (6, 0)],
2: [(1, 1), (4, 0), (6, 1), (9, 0)]
})
else:
assert_equal(
{t: list(tc)
for t, tc in lsa.degree_at(weights=w)}, {
1: [(2, 1), (6, 0)],
2: [(1, 1), (4, 0), (6, 1), (9, 0)]
})
else:
assert_equal(
{t: list(tc)
for t, tc in lsa.degree_at(weights=w)}, {
1: [((2, True), 1), ((5, True), 0)],
2: [((1, True), 1), ((3, True), 0), ((6, True), 1),
((8, True), 0)]
})
na = lsa.degree_at(1, weights=w)
assert isinstance(na, TimeCollection)
if d:
if w:
assert_equal([(t, ns) for t, ns in na], [(2, 1), (3, 2),
(4, 1), (6, 0)])
else:
assert_equal([(t, ns) for t, ns in na], [(2, 1), (6, 0)])
else:
assert_equal([(t, ns) for t, ns in na], [((2, True), 1),
((5, True), 0)])
assert not na.instants
assert len([a for a in na])
assert_equal(lsa.degree_at(1, 2, weights=w), 1)
assert_equal(
{i: n
for i, n in lsa.degree_at(None, 2, 'out', weights=w)}, {
1: 1,
2: 1
})
if d:
if w:
assert_equal(
{
i: [(t, ts) for t, ts in nsa]
for i, nsa in lsa.degree_at(weights=w)
}, {
1: [(2, 1), (3, 2), (4, 1), (6, 0)],
2: [(1, 1), (4, 0), (6, 1), (9, 0)]
})
else:
assert_equal(
{
i: [(t, ts) for t, ts in nsa]
for i, nsa in lsa.degree_at(weights=w)
}, {
1: [(2, 1), (6, 0)],
2: [(1, 1), (4, 0), (6, 1), (9, 0)]
})
else:
assert_equal(
{
i: [(t, ts) for t, ts in nsa]
for i, nsa in lsa.degree_at(weights=w)
}, {
1: [((2, True), 1), ((5, True), 0)],
2: [((1, True), 1), ((3, True), 0), ((6, True), 1),
((8, True), 0)]
})
nsa = NodeSetS({1})
nsb = NodeSetS({2})
ts = TimeSetDF([(1, 4)], discrete=d)
nsma = TemporalNodeSetDF([(1, 1, 4)], discrete=d)
nsmb = TemporalNodeSetDF([(1, 1, 4), (2, 1, 2)], discrete=d)
if d:
assert_equal(set(lsa.substream(nsa, nsb, ts)), {(1, 2, 2, 2, 1),
(1, 2, 3, 3, 2),
(1, 2, 4, 4, 1)})
assert_equal(set(lsa.temporal_neighborhood(nsma, direction='out')),
{(2, 2, 4)})
assert_equal(set(lsa.induced_substream(nsmb)), {(1, 2, 2, 2, 1.0),
(2, 1, 1, 2, 1.0)})
else:
assert_equal(set(lsa.substream(nsa, nsb, ts)),
{(1, 2, 2, 4, 'both', 1)})
assert_equal(set(lsa.temporal_neighborhood(nsma, direction='out')),
{(2, 2, 4, 'both')})
assert_equal(set(lsa.induced_substream(nsmb)),
{(1, 2, 2, 2, 'both', 1.0),
(2, 1, 1, 2, 'both', 1.0)})
try:
lsa | 1
except UnrecognizedTemporalLinkSet:
pass
try:
lsa & 1
except UnrecognizedTemporalLinkSet:
pass
try:
lsa - 1
except UnrecognizedTemporalLinkSet:
pass
df = [(1, 2, 2, 10), (2, 3, 4, 16), (1, 3, 6, 12), (3, 4, 8, 16),
(2, 4, 13, 17)]
assert_equal(
TemporalLinkSetDF(df).get_maximal_cliques(),
{(frozenset({2, 4}), (13, 17)), (frozenset({3, 4}), (8, 16)),
(frozenset({2, 3, 4}), (13, 16)), (frozenset({2, 3}), (4, 16)),
(frozenset({1, 2, 3}), (6, 10)), (frozenset({1, 3}), (6, 12)),
(frozenset({1, 2}), (2, 10))})