def node_is_isolated(self, node, allow_self_loop=False):
"""Checks if the project item with the given name has any connections.
Args:
node (str): Project item name
allow_self_loop (bool): If default (False), Self-loops are considered as an
in-neighbor or an out-neighbor so the method returns False. If True,
single node with a self-loop is considered isolated.
Returns:
bool: True if project item has no in-neighbors nor out-neighbors, False if it does.
Single node with a self-loop is NOT isolated (returns False).
"""
g = self.dag_with_node(node)
if not allow_self_loop:
return nx.is_isolate(g, node)
has_self_loop = g.has_edge(node, node)
if not has_self_loop:
return nx.is_isolate(g, node)
# The node has a self-loop.
# Node degree is the number of edges that are connected to it. A self-loop increases the degree by 2
deg = g.degree(node)
if deg - 2 == 0: # If degree - 2 is zero, it is isolated.
return True
return False
def test_is_isolate():
G = nx.Graph()
G.add_edge(0, 1)
G.add_node(2)
assert not nx.is_isolate(G, 0)
assert not nx.is_isolate(G, 1)
assert nx.is_isolate(G, 2)
def test_is_isolate():
G = nx.Graph()
G.add_edge(0, 1)
G.add_node(2)
assert_false(nx.is_isolate(G, 0))
assert_false(nx.is_isolate(G, 1))
assert_true(nx.is_isolate(G, 2))
def build_graph(self):
# make a partite network
self.graph = nx.random_partition_graph(self.nodes, 0, 0)
if self.p == 0:
return
lp = math.log(self.p)
for p1, p2 in pairwise(self.graph.graph['partition']):
p1 = sorted(p1)
p2 = sorted(p2)
for i in p1:
for j in p2:
if int(self.p) == 1:
self.graph.add_edge(i, j)
continue
lr = math.log(1.0 - random.random())
if lr/lp >= 1:
self.graph.add_edge(i, j)
# prune unnconnected nodes
if self.prune:
for node in self.graph.nodes():
if nx.is_isolate(self.graph, node):
self.graph.remove_node(node)
self.node_labels_to_ints()
self.colour()
def getImportanceGrad(G, x_good, x, nsamples, influ_obj, herd, a):
#Returns the gradient vector of the multilinear relaxation at x as given in Chekuri's paper
#(See Theorem 1 in nips2012 paper)
N = G.number_of_nodes()
grad = Variable(torch.zeros(N))
x_prp = (1 - a)*x + a*x_good
if herd == 1:
samples_list = herd_points(x_prp, nsamples)
else:
samples_list = Variable(torch.bernoulli(x_prp.repeat(nsamples, 1)))
w = getImportanceWeights(samples_list, x, x_prp)
for t in range(nsamples):
sample = samples_list[t]
m = torch.zeros(sample.size())
for p in np.arange(N):
if nx.is_isolate(G, p) is False:
m[p] = 1
grad[p] = grad[p] + w[t]*(influ_obj(np.logical_or(sample.numpy(), m.numpy())) - influ_obj(np.logical_and(sample.numpy(), np.logical_not(m.numpy()))))
m[p] = 0
return grad*1.0/nsamples
def find_valid_alternatives(go, alt_id_g, rel_g):
r = [alt_id for alt_id in find_alternatives(go, alt_id_g)
if alt_id in rel_g and not nx.is_isolate(rel_g, alt_id)]
if len(r) > 1:
print(f'found {len(r)} valid alternatives of {go}', file=sys.stderr)
return r
def set_warranted(self):
undefeated = set([node for (node,x) in self.graph.edges()]) - \
set([node for (x,node) in self.graph.edges()])
undefeated |= set([node for node in self.graph.nodes()
if nx.is_isolate(self.graph, node)])
warranted = undefeated | self.judge.grounded(undefeated, self.graph, set([]), set([]))
for w in warranted:
self.graph.add_node(w, style="filled", fillcolor="green")
self.warranted = warranted
print len(warranted), "reviews were accepted"
def compress(self):
first_stage = set([node for (node, x) in self.dotgraph.edges()]) - set(
[node for (x, node) in self.dotgraph.edges()]
)
first_stage |= set([node for node in self.dotgraph.nodes() if nx.is_isolate(self.dotgraph, node)])
defeat_stages = [first_stage] + self.stages(first_stage, first_stage)
cs = []
for stage in defeat_stages:
cs += self.consistent_subsets(stage, self.warranted)
compressed_dotnodes = {}
has_compressed = {}
compressed_warranted = set([])
compressed_dotgraph = nx.DiGraph()
for subset in cs:
positive_feats = set([])
negative_feats = set([])
for i in subset:
positive_feats |= set(self.dotnodes[i].get_positive_feats())
negative_feats |= set(self.dotnodes[i].get_negative_feats())
r = Review(
"c" + str(self.cid),
{"feats": list(positive_feats), "text": ""},
{"feats": list(negative_feats), "text": ""},
)
compressed_dotnodes[r.id] = r
has_compressed[r.id] = subset
self.cid += 1
if subset.issubset(self.warranted):
compressed_warranted.add(r.id)
compressed_dotgraph.add_node(
r.id, style="filled", fillcolor="green", shape="record", label=str(r.subset_label(subset))
)
else:
compressed_dotgraph.add_node(r.id, shape="record", label=str(r.subset_label(subset)))
for id1, n1 in has_compressed.items():
for id2, n2 in has_compressed.items():
for i in n1:
for j in n2:
ri = self.dotnodes[i]
rj = self.dotnodes[j]
if (
ri.in_conflict(rj)
and not (id1, id2) in compressed_dotgraph.edges()
and not (id2, id1) in compressed_dotgraph.edges()
):
compressed_dotgraph.add_edge(id1, id2, dir="none")
self.warranted = compressed_warranted
self.dotgraph = compressed_dotgraph
self.dotnodes = compressed_dotnodes
self.has_compressed = has_compressed
def generate_migration_links_rates(self):
paths = {}
migs = []
max_migs = []
mindist = 1 # 1km minimum distance in gravity model for improved short-distance asymptotic behavior
dist_cutoff = 20 # beyond 20km effective distance not reached in 1 day.
max_migration_dests = 100 # limit of DTK local migration
for src, v in self.path_lengths:
paths[src] = {}
for dest, dist in v.items():
# print (dist)
# print (src)
# print (dest)
if not dist or src == dest:
continue
if dist < dist_cutoff:
mig_rate = self.coeff * self.graph.population[int(dest)]
mig_volume = self.graph.population[int(src)] * mig_rate
paths[src][dest] = mig_rate
migs.append(mig_rate)
else:
warnings.warn(
'Check if dist_cutoff is too low for source node ' +
str(src) + " distance is " + str(dist))
d = paths[src]
if not d:
warnings.warn('No paths from source ' + str(src) +
' found! Check if node is isolated.')
print("Node " + str(src) + " is isolate " +
str(nx.is_isolate(self.graph, src)))
continue
nl = heapq.nlargest(max_migration_dests, d, key=lambda k: d[k])
# print(len(d), nl, [int(d[k]) for k in nl])
max_migs.append(d[nl[0]])
paths[src] = dict([(k, d[k]) for k in nl])
self.link_rates = paths
return paths
def __walks_multi__(nodes,
net=None,
network_id=None,
steps=10,
number_of_walks=10,
degree=True,
start=None,
probabilistic=True,
weight="weight"):
performed_walks = {}
cn = 0
for node in nodes:
if cn % 100 == 0:
print("walks for node ", cn, "outof", len(nodes))
cn = cn + 1
walks = []
if node in net.nodes():
if not nx.is_isolate(net, node):
if degree:
nw = int(number_of_walks * net.degree[node])
print("running walks", nw, "for node", node)
else:
nw = number_of_walks
walks = global_distances.perform_random_walks(
net,
steps=steps,
number_of_walks=nw,
start=node,
probabilistic=probabilistic,
weight=weight)
#print("count nodes / edges in walk")
#nodes_cnt, edges_cnt = global_distances.rank_walks(net, walks)
performed_walks[node] = walks
return performed_walks
def nodes_filter(x):
i, v = x
return i in nodes and (i is not SINK or not is_isolate(graph, SINK))
def has_isolated_node(g):
for node in g.nodes():
if nx.is_isolate(g, node):
return True
return False
nx.is_directed_acyclic_graph(G) nx.is_aperiodic(G) # distance measure (all for connected graph) nx.center(Gcc) nx.diameter(Gcc) nx.eccentricity(Gcc) nx.periphery(Gcc) nx.radius(Gcc) # flows (seg fault currently) #nx.max_flow(Gcc, 1, 2) #nx.min_cut(G, 1, 2) # isolates nx.is_isolate(G, 1) # False nx.is_isolate(G, 5) # True # HITS nx.hits(G, max_iter=1000) # cannot converge? # maximal independent set nx.maximal_independent_set(G) # shortest path nx.shortest_path(G) # need "predecessors_iter" nx.all_pairs_shortest_path(G) nx.all_pairs_shortest_path_length(G) nx.predecessor(G, 1) nx.predecessor(G, 1, 378)
def Remove_Single_Node(graph):
for node in graph.nodes():
if nx.is_isolate(graph,node):
graph.remove_node(node)
return graph
def filter_node(n):
"""Filter out unconnected nodes."""
return not nx.is_isolate(view, n)
def plot(dsp,
workflow=False,
dot=None,
edge_data=None,
view=False,
depth=-1,
function_module=True,
node_output=True,
nested=False,
**kw_dot):
"""
Plots the Dispatcher with a graph in the DOT language with Graphviz.
:param dsp:
A dispatcher that identifies the model adopted.
:type dsp: dispatcher.Dispatcher
:param dot:
A directed graph in the DOT language.
:type dot: graphviz.dot.Digraph, optional
:param workflow:
If True the workflow graph will be plotted, otherwise the dmap.
:type workflow: bool, (DiGraph, dict), optional
:param edge_data:
Edge attribute to view. The default is the edge weights.
:type edge_data: str, optional
:param node_output:
If True the node outputs are displayed with the workflow.
:type node_output: bool
:param view:
Open the rendered directed graph in the DOT language with the sys
default opener.
:type view: bool, optional
:param depth:
Depth of sub-dispatch plots. If negative all levels are plotted.
:type depth: int, optional
:param function_module:
If True the function labels are plotted with the function module,
otherwise only the function name will be visible.
:type function_module: bool, optional
:param nested:
If False the sub-dispatcher nodes are plotted on the same graph,
otherwise they can be viewed clicking on the node that has an URL
link.
:type nested: bool
:param kw_dot:
Dot arguments:
- name: Graph name used in the source code.
- comment: Comment added to the first line of the source.
- directory: (Sub)directory for source saving and rendering.
- filename: Filename for saving the source (defaults to name + '.gv'
).
- format: Rendering output format ('pdf', 'png', ...).
- engine: Layout command used ('dot', 'neato', ...).
- encoding: Encoding for saving the source.
- graph_attr: Dict of (attribute, value) pairs for the graph.
- node_attr: Dict of (attribute, value) pairs set for all nodes.
- edge_attr: Dict of (attribute, value) pairs set for all edges.
- body: Dict of (attribute, value) pairs to add to the graph
body.
:param kw_dot: dict
:return:
A directed graph source code in the DOT language.
:rtype: graphviz.dot.Digraph
Example:
.. dispatcher:: dsp
:opt: graph_attr={'ratio': '1'}
:code:
>>> from co2mpas.dispatcher import Dispatcher
>>> from co2mpas.dispatcher.utils import SubDispatch, SINK
>>> ss = Dispatcher(name='Sub-sub-dispatcher')
>>> def fun(a):
... return a + 1, a - 1
>>> ss.add_function('fun', fun, ['a'], ['b', 'c'])
'fun'
>>> sub_dispatch = SubDispatch(ss, ['a', 'b', 'c'], output_type='list')
>>> s_dsp = Dispatcher(name='Sub-dispatcher')
>>> s_dsp.add_function('sub_dispatch', sub_dispatch, ['a'], ['b', 'c'])
'sub_dispatch'
>>> dispatch = SubDispatch(s_dsp, ['b', 'c', 'a'], output_type='list')
>>> dsp = Dispatcher(name='Dispatcher')
>>> dsp.add_data('input', default_value={'a': {'a': 3}})
'input'
>>> dsp.add_function('dispatch', dispatch, ['input'], ['d', 'e', SINK])
'dispatch'
>>> dot = plot(dsp, graph_attr={'ratio': '1'})
Dispatch in order to have a workflow:
.. dispatcher:: dsp
:opt: workflow=True, graph_attr={'ratio': '1'}
:code:
>>> o = dsp.dispatch()
...
>>> wf = plot(dsp, workflow=True, graph_attr={'ratio': '1'})
"""
args = _init_graph_data(dsp, workflow, edge_data)
dsp, g, val, dist, edge_data, inputs, outputs = args
dot = dot or _init_dot(dsp, workflow, nested, **kw_dot)
dsp2dot_id = _get_dsp2dot_id(dot, dsp.dmap)
if not g.node:
_set_node(dot, EMPTY, dsp2dot_id)
if START in g.node and (len(g.node) == 1 or not nx.is_isolate(g, START)):
_set_node(dot, START, dsp2dot_id)
elif inputs and set(inputs).issubset(g.node):
dot_u = _set_node(dot, START, dsp2dot_id)
for i, v in enumerate(inputs):
_set_edge(dot, dot_u, dsp2dot_id[v], xlabel=str(i))
for k, v in g.node.items():
if k not in dsp.nodes or (k is SINK and nx.is_isolate(g, SINK)):
continue
_set_node(dot,
k,
dsp2dot_id,
node_attr=dsp.nodes.get(k, {}),
values=val,
dist=dist,
dsp=dsp,
function_module=function_module,
edge_attr=edge_data,
workflow_node=v,
depth=depth,
node_output=node_output,
nested=nested)
for u, v, a in g.edges_iter(data=True):
_set_edge(dot, dsp2dot_id[u], dsp2dot_id[v], a, edge_data=None)
if outputs and set(outputs).issubset(g.node):
dot_v = _set_node(dot, END, dsp2dot_id)
for i, u in enumerate(outputs):
_set_edge(dot, dsp2dot_id[u], dot_v, xlabel=str(i))
if view:
try:
dot.render(cleanup=True, view=True)
except RuntimeError as ex:
log.warning('{}'.format(ex), exc_info=1)
return dot
def stress(G,
output_folder,
fI,
x_constraint=None,
y_constraint=None,
weight_threshold=0):
# remove weak edges
G2 = G.copy()
for ij in G.edges:
if G.edges[ij]['weight'] < weight_threshold:
G2.remove_edge(*ij)
# Make another copy
G3 = G2.copy()
# Check if any nodes have been isolated
if nx.number_of_isolates(G2) != 0:
# First remove nodes from x and y constraints
for i in range(0, len(G2)):
# Check if node is isolated
if nx.is_isolate(G2, i):
# Then check if node has x and y constraints
if i in x_constraint:
del x_constraint[i]
if i in y_constraint:
del y_constraint[i]
# Make iterator over the isolates
iso = nx.isolates(G2)
# Use to remove nodes from graph
G3.remove_nodes_from(iso)
# Mapping should be empty initially
mapping = {}
# Counter
k = 0
# Loop over
for i in range(0, len(G2)):
# If isolate found
if nx.is_isolate(G2, i):
# Increment counter
k = k + 1
else:
# Otherwise save to mapping
mapping[i] = i - k
# Use to relabel nodes and constraints
G3 = nx.relabel_nodes(G3, mapping)
# Loop over keys in mapping
for i in mapping:
# Check if key exists in constraints
if i in x_constraint:
x_constraint[mapping[i]] = x_constraint.pop(i)
if i in y_constraint:
y_constraint[mapping[i]] = y_constraint.pop(i)
# length has decreased by 1 => This isn't the problem
# compute layout
X = _sgd(G3, y_constraint=y_constraint, x_constraint=x_constraint)
# draw with colours
cols_node = list(nx.get_node_attributes(G3, 'color').values())
cols_edge = list(nx.get_edge_attributes(G3, 'color').values())
widths_edge = list(nx.get_edge_attributes(G3, 'width').values())
# Extract x and y labels of nodes
xvs = nx.get_node_attributes(G3, 'x')
yvs = nx.get_node_attributes(G3, 'y')
# Make a copy of xvs to use as labels
labels = yvs.copy()
# Vector containing alphabet
ab = list(string.ascii_lowercase)
# Loop over x values
for i in yvs:
# Check if node is a strain
if xvs[i] == 1.0:
labels[i] = ab[yvs[i] - 1]
nx.draw(G3,
pos=X,
node_color=cols_node,
edge_color=cols_edge,
width=widths_edge,
labels=labels,
arrows=False)
plt.axis('equal')
if weight_threshold != 0.0:
plt.savefig(f'{output_folder}/{G3.graph["name"]}_stress_prun{fI}.png')
else:
plt.savefig(f'{output_folder}/{G3.graph["name"]}_stress{fI}.png')
plt.close()
def remove_edge(u, v):
rm_edge(u, v) # Remove the edge.
if is_isolate(graph, v): # Check if v is isolate.
rm_node(v) # Remove the isolate out node.
def connect_lv_generators(network, allow_multiple_genos_per_load=True):
"""Connect LV generators to existing grids.
This function searches for unconnected generators in all LV grids and
connects them.
It connects
* generators of voltage level 6
* to MV-LV station
* generators of voltage level 7
* with a nom. capacity of <=30 kW to LV loads of type residential
* with a nom. capacity of >30 kW and <=100 kW to LV loads of type
retail, industrial or agricultural
* to the MV-LV station if no appropriate load is available
(fallback)
Parameters
----------
network : :class:`~.grid.network.Network`
The eDisGo container object
allow_multiple_genos_per_load : :obj:`bool`
If True, more than one generator can be connected to one load
Notes
-----
For the allocation, loads are selected randomly (sector-wise) using a
predefined seed to ensure reproducibility.
"""
# get predefined random seed and initialize random generator
seed = int(network.config['grid_connection']['random_seed'])
#random.seed(a=seed)
random.seed(a=1234)
# ToDo: Switch back to 'seed' as soon as line ids are finished, #58
# get standard equipment
std_line_type = network.equipment_data['lv_cables'].loc[
network.config['grid_expansion_standard_equipment']['lv_line']]
std_line_kind = 'cable'
# # TEMP: DEBUG STUFF
# lv_grid_stats = pd.DataFrame(columns=('lv_grid',
# 'load_count',
# 'geno_count',
# 'more_genos_than_loads')
# )
# iterate over all LV grids
for lv_grid in network.mv_grid.lv_grids:
lv_loads = lv_grid.graph.nodes_by_attribute('load')
# counter for genos in v_level 7
log_geno_count_vlevel7 = 0
# generate random list (without replacement => unique elements)
# of loads (residential) to connect genos (P <= 30kW) to.
lv_loads_res = sorted([
lv_load for lv_load in lv_loads
if 'residential' in list(lv_load.consumption.keys())
],
key=lambda _: repr(_))
if len(lv_loads_res) > 0:
lv_loads_res_rnd = set(
random.sample(lv_loads_res, len(lv_loads_res)))
else:
lv_loads_res_rnd = None
# generate random list (without replacement => unique elements)
# of loads (retail, industrial, agricultural) to connect genos
# (30kW < P <= 100kW) to.
lv_loads_ria = sorted([
lv_load for lv_load in lv_loads if any([
_ in list(lv_load.consumption.keys())
for _ in ['retail', 'industrial', 'agricultural']
])
],
key=lambda _: repr(_))
if len(lv_loads_ria) > 0:
lv_loads_ria_rnd = set(
random.sample(lv_loads_ria, len(lv_loads_ria)))
else:
lv_loads_ria_rnd = None
for geno in sorted(lv_grid.graph.nodes_by_attribute('generator'),
key=lambda x: repr(x)):
if nx.is_isolate(lv_grid.graph, geno):
lv_station = lv_grid.station
# generator is of v_level 6 -> connect to LV station
if geno.v_level == 6:
line_length = calc_geo_dist_vincenty(
network=network,
node_source=geno,
node_target=lv_station)
line = Line(id=random.randint(10**8, 10**9),
length=line_length / 1e3,
quantity=1,
kind=std_line_kind,
type=std_line_type,
grid=lv_grid)
lv_grid.graph.add_edge(geno,
lv_station,
line=line,
type='line')
# add line to equipment changes to track costs
_add_cable_to_equipment_changes(network=network, line=line)
# generator is of v_level 7 -> assign geno to load
elif geno.v_level == 7:
# counter for genos in v_level 7
log_geno_count_vlevel7 += 1
# connect genos with P <= 30kW to residential loads, if available
if (geno.nominal_capacity <= 30) and (lv_loads_res_rnd
is not None):
if len(lv_loads_res_rnd) > 0:
lv_load = lv_loads_res_rnd.pop()
# if random load list is empty, create new one
else:
lv_loads_res_rnd = set(
random.sample(lv_loads_res, len(lv_loads_res)))
lv_load = lv_loads_res_rnd.pop()
# get cable distributor of building
lv_conn_target = list(
lv_grid.graph.neighbors(lv_load))[0]
if not allow_multiple_genos_per_load:
# check if there's an existing generator connected to the load
# if so, select next load. If no load is available, connect to station.
while any([
isinstance(_, Generator)
for _ in lv_grid.graph.neighbors(
list(lv_grid.graph.neighbors(lv_load))
[0])
]):
if len(lv_loads_res_rnd) > 0:
lv_load = lv_loads_res_rnd.pop()
# get cable distributor of building
lv_conn_target = list(
lv_grid.graph.neighbors(lv_load))[0]
else:
lv_conn_target = lv_grid.station
logger.debug(
'No valid conn. target found for {}. '
'Connected to {}.'.format(
repr(geno), repr(lv_conn_target)))
break
# connect genos with 30kW <= P <= 100kW to residential loads
# to retail, industrial, agricultural loads, if available
elif (geno.nominal_capacity > 30) and (lv_loads_ria_rnd
is not None):
if len(lv_loads_ria_rnd) > 0:
lv_load = lv_loads_ria_rnd.pop()
# if random load list is empty, create new one
else:
lv_loads_ria_rnd = set(
random.sample(lv_loads_ria, len(lv_loads_ria)))
lv_load = lv_loads_ria_rnd.pop()
# get cable distributor of building
lv_conn_target = list(
lv_grid.graph.neighbors(lv_load))[0]
if not allow_multiple_genos_per_load:
# check if there's an existing generator connected to the load
# if so, select next load. If no load is available, connect to station.
while any([
isinstance(_, Generator)
for _ in lv_grid.graph.neighbors(
list(lv_grid.graph.neighbors(lv_load))
[0])
]):
if len(lv_loads_ria_rnd) > 0:
lv_load = lv_loads_ria_rnd.pop()
# get cable distributor of building
lv_conn_target = list(
lv_grid.graph.neighbors(lv_load))[0]
else:
lv_conn_target = lv_grid.station
logger.debug(
'No valid conn. target found for {}. '
'Connected to {}.'.format(
repr(geno), repr(lv_conn_target)))
break
# fallback: connect to station
else:
lv_conn_target = lv_grid.station
logger.debug('No valid conn. target found for {}. '
'Connected to {}.'.format(
repr(geno), repr(lv_conn_target)))
line = Line(id=random.randint(10**8, 10**9),
length=1e-3,
quantity=1,
kind=std_line_kind,
type=std_line_type,
grid=lv_grid)
lv_grid.graph.add_edge(geno,
lv_station,
line=line,
type='line')
# add line to equipment changes to track costs
_add_cable_to_equipment_changes(network=network, line=line)
# warn if there're more genos than loads in LV grid
if log_geno_count_vlevel7 > len(lv_loads):
logger.debug(
'The count of newly connected generators in voltage level 7 ({}) '
'exceeds the count of loads ({}) in LV grid {}.'.format(
str(log_geno_count_vlevel7), str(len(lv_loads)),
repr(lv_grid)))
def make_graphs(G,window,index,communities,commoner_graphs):
"""
Generate JSON for NetworkX graph. Update commoner graphs.
This method generates all necessary information from a NetworkX
graph representation and returns it in a JSON format. It also
updates the 'dynamic communities' and individual commoner graphs
(using make_dynamic_communities and build_commoner_data methods)
:param G: NetworkX graph of interactions in time window
:param window: 2-tuple containing start and end dates
:param index: integer representing time step
:param communities: list holding NetworkX dynamic communities
(filled in by make_dynamic_communities method)
:param commoner_graphs: dictionary mapping each commoner node to
its interaction history (filled in by build_commoner_data method)
:returns: tuple containing:
1. Updated dynamic communities
2. Updated commoner_graphs
3. JSON representation of NetworkX graph
4. Updated NetworkX graph
"""
edges_to_remove = []
tag_edges = []
#tag_nodes = {}
tag_counts = {} #Holds counts of each of the tags
cumulative = (index == 0)
create_count = 0
comment_count = 0
convo_count = 0
trans_count = 0
graph_copy = copy.deepcopy(G) #To avoid screwing future iterations
nodeiter = G.nodes(data=True)
edgeiter = G.edges(data=True)
#Filter edges outside time window and add count stats
for (u,v,c) in edgeiter:
c['activations'] = []
if window[0] is not None:
edge_exists = False
for intervals in c['spells']:
if (window[0] <= cf.to_date(intervals[0]) < window[1]):
edge_exists = True
break
else:
edge_exists = True #Edge always exists in static network
if edge_exists == False:
edges_to_remove.append((u,v,c))
else:
copy_edge = graph_copy.edges[u,v]
if window[0] is not None:
copy_edge['first_active'] = copy_edge['spells'][0][0]
copy_edge['last_active'] = copy_edge['spells'][len(copy_edge['spells'])-1][0]
del graph_copy.edges[u,v]['spells']
#Remove non-existent edges
graph_copy.remove_edges_from(edges_to_remove)
#Also remove the tag edges so not to influence k-core calculation
graph_copy.remove_edges_from((tag_edges))
#Filter nodes outside the time window
nodes_to_remove = []
zero_nodes = []
if window[0] is not None:
for (n,c) in nodeiter:
graph_copy.nodes[n]['nodemeta'] = []
graph_copy.nodes[n]['date'] = cf.to_str(window[0])
c['date'] = cf.to_str(window[0]) #TODO: Do both lines need to be here?
if nx.is_isolate(graph_copy,n):
nodes_to_remove.append(n)
G.nodes[n]['binary_active'] += "0"
graph_copy.nodes[n]['binary_active'] += "0"
if 'type' not in c or c['type'] == cf.user_type:
zero_nodes.append((n,c))
else:
G.nodes[n]['times_active'] += 1
graph_copy.nodes[n]['times_active'] += 1
G.nodes[n]['binary_active'] += "1"
graph_copy.nodes[n]['binary_active'] += "1"
graph_copy.remove_nodes_from(nodes_to_remove)
#Get rid of spells and actions that fall outside the window range
graph_copy = filter_spells(graph_copy,window)
#DO THE KCORE CALCULATIONS HERE
(core_G,colluders) = dx.weighted_core(graph_copy.to_undirected(),window,cumulative)
#Add the tags back in
core_G.add_edges_from(tag_edges)
to_remove = []
nodeiter = core_G.nodes(data=True)
for (n,c) in nodeiter:
if cf.user_type != '' and 'type' not in c: #If there are meant to be types but we can't find any
to_remove.append(n)
core_G.remove_nodes_from(to_remove)
#Recommender data is built from the cumulative graph
if not cumulative:
build_commoner_data(core_G,commoner_graphs,zero_nodes)
#Remove isolated nodes that exist after removing Basic Income
core_G.remove_nodes_from(list(nx.isolates(core_G)))
#Now compare fronts to previous partitions
if not cumulative:
partition = make_dynamic_communities(core_G,communities,index)
else:
undirectedGraph = core_G.to_undirected()
partition = community.best_partition(undirectedGraph,weight='positivemax')
nodeiter = core_G.nodes(data=True)
for n,c in nodeiter:
c['cluster'] = partition[n]
if cf.LABEL_KEY != "":
c['label'] = c[cf.LABEL_KEY]
else:
c['label'] = str(n)
core_graph_json = json_graph.node_link_data(core_G)
if window[1] is not None:
meta_info = {'date':cf.to_str(window[1]),'colluders':colluders}
core_graph_json.update(meta_info)
return (communities,commoner_graphs,core_graph_json,core_G)
def connect_mv_generators(network):
"""Connect MV generators to existing grids.
This function searches for unconnected generators in MV grids and connects them.
It connects
* generators of voltage level 4
* to HV-MV station
* generators of voltage level 5
* with a nom. capacity of <=30 kW to LV loads of type residential
* with a nom. capacity of >30 kW and <=100 kW to LV loads of type
retail, industrial or agricultural
* to the MV-LV station if no appropriate load is available (fallback)
Parameters
----------
network : :class:`~.grid.network.Network`
The eDisGo container object
Notes
-----
Adapted from `Ding0 <https://github.com/openego/ding0/blob/\
21a52048f84ec341fe54e0204ac62228a9e8a32a/\
ding0/grid/mv_grid/mv_connect.py#L820>`_.
"""
# get params from config
buffer_radius = int(
network.config['grid_connection']['conn_buffer_radius'])
buffer_radius_inc = int(
network.config['grid_connection']['conn_buffer_radius_inc'])
# get standard equipment
std_line_type = network.equipment_data['mv_cables'].loc[
network.config['grid_expansion_standard_equipment']['mv_line']]
for geno in sorted(network.mv_grid.graph.nodes_by_attribute('generator'),
key=lambda _: repr(_)):
if nx.is_isolate(network.mv_grid.graph, geno):
# ===== voltage level 4: generator has to be connected to MV station =====
if geno.v_level == 4:
line_length = calc_geo_dist_vincenty(
network=network,
node_source=geno,
node_target=network.mv_grid.station)
line = Line(id=random.randint(10**8, 10**9),
type=std_line_type,
kind='cable',
quantity=1,
length=line_length / 1e3,
grid=network.mv_grid)
network.mv_grid.graph.add_edge(network.mv_grid.station,
geno,
line=line,
type='line')
# add line to equipment changes to track costs
_add_cable_to_equipment_changes(network=network, line=line)
# ===== voltage level 5: generator has to be connected to MV grid (next-neighbor) =====
elif geno.v_level == 5:
# get branches within a the predefined radius `generator_buffer_radius`
branches = calc_geo_lines_in_buffer(
network=network,
node=geno,
grid=network.mv_grid,
radius=buffer_radius,
radius_inc=buffer_radius_inc)
# calc distance between generator and grid's lines -> find nearest line
conn_objects_min_stack = _find_nearest_conn_objects(
network=network, node=geno, branches=branches)
# connect!
# go through the stack (from nearest to most far connection target object)
generator_connected = False
for dist_min_obj in conn_objects_min_stack:
target_obj_result = _connect_mv_node(
network=network, node=geno, target_obj=dist_min_obj)
if target_obj_result is not None:
generator_connected = True
break
if not generator_connected:
logger.debug(
'Generator {0} could not be connected, try to '
'increase the parameter `conn_buffer_radius` in '
'config file `config_grid.cfg` to gain more possible '
'connection points.'.format(geno))
def nodes_filter(x):
i, v = x
return i in nodes and (i is not SINK
or not is_isolate(graph, SINK))
def helper_walks(networks,
nodes,
network_ids,
steps=10,
number_of_walks=10,
degree=True,
probabilistic=True,
weight="weight"):
"""
Estimates for networks number_of_walks walks of size steps.
Parameters:
networks (list): of networkX graph objects
nodes (list): of nodes (areas) to be compared.
network_ids (list): list of network IDs.
steps (int): is size of random walk
number_of_walks (int): how many random walks are performed on G
degree (boolean): if True then the number of random walks performed for each starting node is dependent on its degree and is estimated as degree*number_of_walks.
probabilisitc (boolean): if True edge weights are taken into account else all edges are considered equal. If true then weight needs to be set
weight (str): edge attribute name as contained in G. Weight is evaluated as a similarity
Returns:
walks (dict): key is network IDs and value is dict where key is starting node and value is list of performed walks.
Each walk is a sublist and contains the node IDs in order of their visit by the random walk.
"""
performed_walks = {}
for net_id in network_ids:
performed_walks[net_id] = {}
cn = 0
for node in nodes:
if cn % 100 == 0:
print("walks for node ", cn, "outof", len(nodes))
cn = cn + 1
walks = []
for i in range(len(networks)):
net = networks[i]
network_id = network_ids[i]
if node in net.nodes():
if not nx.is_isolate(net, node):
if degree:
nw = int(number_of_walks * net.degree[node])
print("running walks", nw, "for node", node)
else:
nw = number_of_walks
walks = global_distances.perform_random_walks(
net,
steps=steps,
number_of_walks=nw,
start=node,
probabilistic=probabilistic,
weight=weight)
#save
performed_walks[network_id][node] = walks
return performed_walks
def __getSourceNodes(self):
sourceNodes = [
node for (node, indegree) in self.G.in_degree()
if (indegree == 0 and not nx.is_isolate(self.G, node))
]
return sourceNodes
nx.is_directed_acyclic_graph(G) nx.is_aperiodic(G) # distance measure (all for connected graph) nx.center(Gcc) nx.diameter(Gcc) nx.eccentricity(Gcc) nx.periphery(Gcc) nx.radius(Gcc) # flows (seg fault currently) #nx.max_flow(Gcc, 1, 2) #nx.min_cut(G, 1, 2) # isolates nx.is_isolate(G, 1) # False nx.is_isolate(G, 5) # True # HITS nx.hits(G,max_iter=1000) # cannot converge? # maximal independent set nx.maximal_independent_set(G) # shortest path nx.shortest_path(G) # need "predecessors_iter" nx.all_pairs_shortest_path(G) nx.all_pairs_shortest_path_length(G) nx.predecessor(G, 1) nx.predecessor(G, 1, 378)
def load_data(self,file_name="1"):
""" This method loads existing data into a graph object.
"""
self.contexts_list = {}
# Loading in data other than the Database
other = open("./"+str(file_name)+"_general_data.csv", "r+")
for line in other:
data = line.strip().split(";")
if data[0] == "my_ID":
if data[1] == 'False':
self.my_ID = -1
else:
self.my_ID = int(data[1])
self.mynet.add_node(self.my_ID)
if data[0] == "contexts_list":
self.contexts_list = {}
if data[1] != "":
new_data = data[1].split(",")
for i in new_data:
if i != "":
j = i.split(":")
self.contexts_list[int(j[0])] = j[1]
if data[0] == "defined_colors":
self.defined_colors = [i for i in data[1].split(",")]
# loading in the database
self.friend_db = pd.read_csv("./"+str(file_name)
+"_friend_data.csv", header=0, index_col=0)
new_names = []
for i in list(self.friend_db.columns.values):
new_names.append(int(i))
if self.my_ID != i:
# adding network ties
self.mynet.add_node(int(i))
self.mynet.add_edge(int(i), self.my_ID)
self.friend_db.columns = new_names
attrs=list(self.friend_db.index.values)
# adding the mutual ties connections
#and converting to ints from strings)
for i in list(self.friend_db.columns.values):
q = self.friend_db[int(i)]["known from"]
if q==q:
self.friend_db[int(i)]["known from"] = int(q)
r = self.friend_db[int(i)]["race"]
if r==r:
self.friend_db[int(i)]["race"] = int(r)
p = self.friend_db[int(i)]["gender"]
if p==p:
self.friend_db[int(i)]["gender"] = str(p)
if type(self.friend_db[int(i)]["mutuals"]) == str:
mut_str = self.friend_db[int(i)]["mutuals"][1:-1].split(",")
mutuals = []
for j in mut_str:
try:
mutuals.append(int(j))
except:
if j != "":
mutuals.append(long(j))
else:
pass
self.friend_db[int(i)]["mutuals"] = mutuals
for j in mutuals:
if int(j) in new_names:
try:
edge = self.mynet[int(j)][int(i)]
except:
self.mynet.add_edge(int(j), int(i))
val = self.friend_db[int(i)]["strong tie"]
if val != "" and (val==val):
self.mynet[int(i)][self.my_ID]["strong"] = int(val)
for edge in self.mynet.edges():
try:
self.mynet[int(edge[0])][int(edge[1])]["strong"]
except:
self.mynet[int(edge[0])][int(edge[1])]["strong"]=0
isos = []
bu = nx.Graph()
bu.add_nodes_from(self.mynet.nodes())
bu.add_edges_from(self.mynet.edges())
bu.remove_node(self.my_ID)
for n in bu.nodes():
if nx.is_isolate(bu, n):
bu.remove_node(n)
isos.append(n)
self.no_ego_net = bu
self.b_cent = self.betweenness_centrality(withme=False)
self.c_cent = self.closeness_centrality(withme=False)
self.d_cent = self.degree_centrality(withme=False)
self.e_cent = self.eigenvector_centrality(iterations=100,withme=False)
for iso in isos:
self.b_cent[iso]=-1
self.c_cent[iso]=-1
self.d_cent[iso]=-1
self.e_cent[iso]=-1
self.b_cent_w = self.betweenness_centrality(withme=True)
self.c_cent_w = self.closeness_centrality(withme=True)
self.d_cent_w = self.degree_centrality(withme=True)
self.e_cent_w = self.eigenvector_centrality(iterations=100,withme=True)
print "Data successfully loaded"
def _not_isolated_nodes(G):
return filter(lambda n: not nx.is_isolate(G, n), G.nodes)
def __init__(self,
obj,
workflow=False,
nested=True,
edge_data=(),
node_data=(),
node_function=(),
draw_outputs=0,
view=False,
node_styles=None,
depth=-1,
function_module=False,
name=None,
comment=None,
directory=None,
filename=None,
format='svg',
engine=None,
encoding=None,
graph_attr=None,
node_attr=None,
edge_attr=None,
body=None,
parent_dot='',
_saved_outputs=None):
"""
Plots the Dispatcher with a graph in the DOT language with Graphviz.
:param workflow:
If True the latest solution will be plotted, otherwise the dmap.
:type workflow: bool, optional
:param view:
Open the rendered directed graph in the DOT language with the sys
default opener.
:type view: bool, optional
:param nested:
If False the sub-dispatcher nodes are plotted on the same graph,
otherwise they can be viewed clicking on the node that has an URL
link.
:type nested: bool, optional
:param edge_data:
Edge attributes to view.
:type edge_data: tuple[str], optional
:param node_data:
Data node attributes to view.
:type node_data: tuple[str], optional
:param node_function:
Function node attributes to view.
:type node_function: tuple[str], optional
:param draw_outputs:
It modifies the defaults data node and edge attributes to view.
If `draw_outputs` is:
- 1: node attribute 'output' is drawn.
- 2: edge attribute 'value' is drawn.
- 3: node 'output' and edge 'value' attributes are drawn.
- otherwise: node 'output' and edge 'value' attributes are not
drawn.
:type draw_outputs: int, optional
:param node_styles:
Default node styles according to graphviz node attributes.
:type node_styles: dict[str|Token, dict[str, str]]
:param depth:
Depth of sub-dispatch plots. If negative all levels are plotted.
:type depth: int, optional
:param function_module:
If True the function labels are plotted with the function module,
otherwise only the function name will be visible.
:type function_module: bool, optional
:param name:
Graph name used in the source code.
:type name: str
:param comment:
Comment added to the first line of the source.
:type comment: str
:param directory:
(Sub)directory for source saving and rendering.
:type directory: str, optional
:param filename:
File name for saving the source.
:type filename: str, optional
:param format:
Rendering output format ('pdf', 'png', ...).
:type format: str, optional
:param engine:
Layout command used ('dot', 'neato', ...).
:type engine: str, optional
:param encoding:
Encoding for saving the source.
:type encoding: str, optional
:param graph_attr:
Dict of (attribute, value) pairs for the graph.
:type graph_attr: dict, optional
:param node_attr:
Dict of (attribute, value) pairs set for all nodes.
:type node_attr: dict, optional
:param edge_attr:
Dict of (attribute, value) pairs set for all edges.
:type edge_attr: dict, optional
:param body:
Dict of (attribute, value) pairs to add to the graph body.
:type body: dict, optional
:return:
A directed graph source code in the DOT language.
:rtype: graphviz.dot.Digraph
Example:
.. dispatcher:: dsp
:opt: graph_attr={'ratio': '1'}
:code:
>>> from co2mpas.dispatcher import Dispatcher
>>> dsp = Dispatcher(name='Dispatcher')
>>> def fun(a):
... return a + 1, a - 1
>>> dsp.add_function('fun', fun, ['a'], ['b', 'c'])
'fun'
>>> dsp.plot(view=False, graph_attr={'ratio': '1'})
<co2mpas.dispatcher.utils.drw.DspPlot object at 0x...>
"""
from .sol import Solution
from .. import Dispatcher
from networkx import is_isolate
self._edge_data = edge_data
self._node_data = node_data
self._node_function = node_function
self._graph_attr = graph_attr
self._node_attr = node_attr
self._edge_attr = edge_attr
self._body = body
self.node_styles = _upt_styles(node_styles or {}, self.__node_styles)
self.depth = depth
self.draw_outputs = draw_outputs
self.function_module = function_module
self._saved_outputs = _saved_outputs or {}
self.workflow = workflow
inputs, outputs = (), ()
obj = parent_func(obj)
if isinstance(obj, Solution):
dsp, sol = obj.dsp, obj
elif isinstance(obj, SubDispatchFunction):
dsp, sol = obj.dsp, obj.solution
inputs, outputs = obj.inputs or (), obj.outputs or ()
elif isinstance(obj, SubDispatch):
dsp, sol = obj.dsp, obj.solution
if obj.output_type != 'all':
outputs = obj.outputs or ()
elif isinstance(obj, Dispatcher):
dsp, sol = obj, obj.solution
else:
raise ValueError('Type %s not supported.' % type(obj).__name__)
self.dsp = dsp
_body = self.__body.copy()
if workflow:
_body['label'] = '<%s workflow>'
self.g = g = sol.workflow
self.obj = sol
else:
_body['label'] = '<%s>'
self.g = g = dsp.dmap
self.obj = obj
draw_outputs = int(draw_outputs)
if draw_outputs == 1:
i, j = -1, None
elif draw_outputs == 2:
i, j = None, -1
elif draw_outputs == 3:
i = j = None
else:
i = j = -1
self.node_data = node_data or self.__node_data[:j]
self.node_function = node_function or self.__node_function
self.edge_data = tuple(k if k != 'weight' else dsp.weight
for k in edge_data or self.__edge_data[:i])
name = name or dsp.name or '%s %d' % (type(dsp).__name__, id(dsp))
self.nested = nested
if filename:
if directory is not None:
filename = osp.join(directory, filename)
directory, filename = osp.split(osp.abspath(filename))
else:
if directory is None:
directory = mkdtemp('')
filename = _encode_file_name(name[8:] if parent_dot else name)
if osp.splitext(filename)[1] != self._default_extension:
filename = '%s.%s' % (filename, self._default_extension)
name = self._html_encode(name)
_body['label'] = _body['label'] % name
body = combine_dicts(_body, body or {})
super(DspPlot, self).__init__(
name=name,
comment=comment,
filename=filename,
directory=directory,
format=format,
engine=engine,
encoding=encoding,
graph_attr=combine_dicts(self.__graph_attr, graph_attr or {}),
node_attr=combine_dicts(self.__node_attr, node_attr or {}),
edge_attr=combine_dicts(self.__edge_attr, edge_attr or {}),
body=['%s = %s' % (k, v) for k, v in body.items()])
self.id_map = self.get_id_map(parent_dot,
chain(g.node, inputs, outputs))
if not g.node or not (g.edge or inputs or outputs):
self._set_data_node(EMPTY, {})
if START in g.node or (inputs and START not in g.node):
self._set_data_node(START, {})
if outputs and END not in g.node:
self._set_data_node(END, {})
for k, v in sorted(g.node.items()):
if k not in dsp.nodes or (k is SINK and is_isolate(g, SINK)):
continue
self._set_node(k, v)
edges = {(u, v): a for u, v, a in g.edges_iter(data=True)}
for i, v in enumerate(inputs):
n = (START, v)
edges[n] = combine_dicts(edges.get(n, {}), {'inp_id': i})
for i, u in enumerate(outputs):
n = (u, END)
edges[n] = combine_dicts(edges.get(n, {}), {'out_id': i})
for (u, v), a in sorted(edges.items()):
self._set_edge(u, v, a)
if view:
self.render(cleanup=True, view=True)
def remove_edge(u, v):
rm_edge(u, v) # Remove the edge.
if is_isolate(graph, v): # Check if v is isolate.
rm_node(v) # Remove the isolate out node.
def main():
global seqDict
global rna_gap
global g
global args
global scaffolding_type_1
global scaffolding_type_2
"""
Parsing arguments and initiating variables
"""
if args.d:
logging.basicConfig(format='%(asctime)s::%(levelname)s::%(message)s',filename=args.log, level=logging.DEBUG)
elif args.v:
logging.basicConfig(format='%(asctime)s::%(levelname)s::%(message)s',filename=args.log, level=logging.INFO)
else:
logging.basicConfig(format='%(asctime)s::%(levelname)s::%(message)s',filename=args.log)
rna_gap=int(args.rna_gap)
logging.info("Begin the analysis with Scaff2link version "+version)
mkdir_p(args.outDir)
# pour avoir les arguments c'est args.fasta / args.phylo / args.rna etc ... ça te renvoie la string (type string)
g=nx.MultiDiGraph()
## Adding all vertexes to the graph
"""
Reading fasta
"""
seqDict=dict()
for record in SeqIO.parse(args.fasta, "fasta"):
seqDict[record.id]=record.seq
g.add_node(record.id,
length=len(record.seq),
log10Len=math.log10(len(record.seq)),
strand='')
logging.info("Number of nodes parsed: "+str(len(g.nodes)))
"""
Adding edges from Ragout :
"""
logging.info("Parsing edges from synteny information using ragout: "+args.phylo)
scaffolding_type_1="synteny"
current_scaffold=""
fromName=""
gap_length=0
with open(args.phylo) as f:
for line in f:
line=line.rstrip("\n")
if line[0] != "#" and line !="":
elems=line.split("\t")
if current_scaffold == elems[0]:
if elems[4] == "N":
gap_length=int(elems[5])
else:
if not (fromName in g.nodes and elems[5] in g.nodes):
logging.critical("Unknown node name when parsing ragout edge from "+fromName+" to "+elems[5])
sys.exit(1)
g.add_edge(fromName,elems[5],type=scaffolding_type_1,fromStrand=from_orientation,toStrand=elems[8],gap=gap_length, readsCount=-1)
g.nodes[fromName]["strand"]=from_orientation
g.nodes[elems[5]]["strand"]=elems[8]
fromName=elems[5]
from_orientation=elems[8]
else:
current_scaffold=elems[0]
fromName=elems[5]
from_orientation=elems[8]
"""
Parsing edges from Agouti :
"""
logging.info("Parsing joint pairs in edges from rna-seq information using agouti: "+args.rna)
scaffolding_type_2="rna-seq"
joint_pairs=dict()
with open(args.rna) as f:
for line in f:
line=line.rstrip()
elems=line.split("\t")
fromName=elems[1]
toName=elems[4]
if args.lib_type[0]=='f':
FromStrand=elems[3]
else:
FromStrand=strand_reverse(elems[3])
if args.lib_type[1]=='f':
ToStrand=elems[6]
else:
ToStrand=strand_reverse(elems[6])
key="\t".join([FromStrand,fromName,ToStrand,toName])
if key not in joint_pairs.keys():
joint_pairs[key]=1
else:
joint_pairs[key]+=1
"""
Adding parsed edges from Agouti :
"""
logging.info("Adding rna-seq based edges to the scaffolding graph")
for key in joint_pairs.keys():
if joint_pairs[key]>(int(args.min_reads)-1):
key_list=key.split("\t")
FromStrand=key_list[0]
fromName=key_list[1]
ToStrand=key_list[2]
toName=key_list[3]
if not (fromName in g.nodes and toName in g.nodes):
logging.critical("Unknown node name when parsing agouti edge from "+fromName+" to "+toName)
sys.exit(1)
# Here we add the edge, but try to simplify it if it already exist because of Ragout
numFromTo=g.number_of_edges(fromName,toName)
add_stranded_edge(fromName,toName,FromStrand,ToStrand,scaffolding_type_2,joint_pairs[key])
"""
Finalizing and reporting the initial graph (end of step 00)
"""
logging.info("Scaffolding graph completed, collecting nodes statistics")
TotalNodes=len(g.nodes)
ConnectedNodes=TotalNodes
ConsistantNodes=TotalNodes
node2remove=list()
for node in g.nodes:
if nx.is_isolate(g,node): # WAY TO GET THE DEGREE OF NODE
#g.remove_node(node)
node2remove.append(node)
ConnectedNodes-=1
ConsistantNodes-=1
elif g.nodes[node]["strand"]=='.':
ConsistantNodes-=1
for node in node2remove:
g.remove_node(node)
logging.info("Out of "+str(TotalNodes)+" initial nodes, "+str(ConnectedNodes)+" are connected, and among them "+str(ConsistantNodes)+" are strand consistent")
if args.v :
logging.info("Collecting edges statistics")
edgeCounter=Counter(list(g.edges))
t1=0
t2=0
t12=0
edges_list=list(g.edges)
edges_set=set(g.edges)
for edge in edgeCounter.keys():
for key in range(0,edgeCounter[edge]):
if g[edge[0]][edge[1]][key]["type"]==scaffolding_type_1:
t1+=1
elif g[edge[0]][edge[1]][key]["type"]==scaffolding_type_2:
t2+=1
elif g[edge[0]][edge[1]][key]["type"]==scaffolding_type_1+"_AND_"+scaffolding_type_2:
t12+=1
else:
logging.critical("unknown scaffold type")
sys.exit(1)
logging.info("Edges statistics : \n"+
";".join([scaffolding_type_1,scaffolding_type_2,scaffolding_type_1+"_AND_"+scaffolding_type_2])+
"\n"+";".join([str(t1),str(t2),str(t12)]))
mkdir_p(args.outDir+'/00-complete_graph')
nx.write_graphml(g, args.outDir+"/00-complete_graph/graph.graphml")
"""
Step 01 : chain simplification
"""
logging.info("starting first chain simplification")
setNodes=set(g.nodes)
basename="scaff2links_chain_"
count_name=1
while len(setNodes)!=0:
try:
node=setNodes.pop()
if g.nodes[node]["strand"]!='.':
inspectIn=True
inspectOut=True
chain=[node]
nodeIn=node
nodeOut=node
logging.debug("Chain simplification started on node:"+node)
while inspectIn:
if len(g.in_edges(nodeOut))==1 :
possibleNodeOut=list(g.in_edges(nodeOut))[0][0]
if g.has_edge(nodeOut,possibleNodeOut):
inspectIn=False
logging.debug("STOP")
else:
logging.debug("IN chain extension: (in)"+nodeOut+"\t(out)"+possibleNodeOut)
if (len(g.out_edges(possibleNodeOut))==1) and (g.nodes[possibleNodeOut]["strand"]!='.'):
logging.debug("CONTINUE")
nodeOut=possibleNodeOut
chain.append(nodeOut)
setNodes.remove(nodeOut)
else :
inspectIn=False
logging.debug("STOP")
else: inspectIn=False
while inspectOut:
if len(g.out_edges(nodeIn))==1 :
possibleNodeIn=list(g.out_edges(nodeIn))[0][1]
if g.has_edge(possibleNodeIn,nodeIn):
inspectOut=False
logging.debug("STOP")
else:
logging.debug("OUT chain extension: (out)"+nodeIn+"\t(in)"+possibleNodeIn)
if (len(g.in_edges(possibleNodeIn))==1) and (g.nodes[possibleNodeIn]["strand"]!='.'):
logging.debug("CONTINUE")
nodeIn=possibleNodeIn
chain.append(nodeIn)
setNodes.remove(nodeIn)
else:
inspectOut=False
logging.debug("STOP")
else: inspectOut=False
name=basename+str(count_name)
count_name+=1
def_chain=chain_simplification(chain=chain,start=nodeOut,end=nodeIn,name=name,chain_is_path=False)
logging.debug(name+"\t"+def_chain)
except:
nx.write_graphml(g, args.outDir+"/error.graphml")
print(traceback.format_exc())
logging.critical("error during first chain simplification, the current graph have been written")
sys.exit(1)
"""
end of step 01 writing...
"""
mkdir_p(args.outDir+'/01-simplified_graph')
nx.write_graphml(g, args.outDir+"/01-simplified_graph/graph.graphml")
node2remove=list()
for node in g.nodes:
if nx.is_isolate(g,node): # WAY TO GET THE DEGREE OF NODE
#g.remove_node(node)
node2remove.append(node)
for node in node2remove:
g.remove_node(node)
with open(args.outDir+"/01-simplified_graph/scaffolds.fasta", "w") as output_handle:
for key in seqDict.keys():
SeqIO.write(SeqRecord(seqDict[key],id=key,description=''), output_handle, "fasta")
"""
Step 02 : dag simplification
"""
logging.info("starting dag simplification")
logging.info("Find remaining bridges")
g_broken=nx.Graph(g.copy())
edge2remove=list()
for e in nx.bridges(g_broken):
edge2remove.append(e)
for e_ in edge2remove:
g_broken.remove_edge(e_[0],e_[1])
basename="scaff2links_dag_"
count_name=1
logging.info("Connected component analysis for DAG")
for nset in nx.connected_components(g_broken):
subg=g.subgraph(nset)
if nx.is_directed_acyclic_graph(subg) and len(list(subg.nodes()))>1:
lpath=nx.dag_longest_path(subg)
logging.debug("DAG found :"+str(nset)+"| type="+str(type(nset)))
if len(list(subg.nodes()))==2:
logging.warn("A connected component resulting from the graph where all bridges have been removed is of size 2, which is mathematically unexpected")
if len(lpath)==len(nset):
logging.debug("DAG with all nodes in the longest path: ("+")->-(".join(lpath)+')')
# check neighbor
pos=-1
start=0
for node in lpath:
pos+=1
add_set=set()
if node==lpath[0] or pos == start:
for e in list(g.in_edges(node)):
add_set.add(e[0])
if node==lpath[-1]:
for e in list(g.out_edges(node)):
add_set.add(e[1])
if not (set(nx.all_neighbors(g,node)) <= (add_set | nset)):
logging.debug("Found cutting node in DAG: "+lpath[pos])
for e in list(g.out_edges(node)):
add_set.add(e[1])
if (set(nx.all_neighbors(g,node)) <= (add_set | nset)) and (pos-start>0):
logging.debug("cutted DAG simplification (including the cutting node) at "+lpath[pos]+": ("+")->-(".join([ lpath[x] for x in range(start,pos+1) ] )+')')
chain_simplification([ lpath[x] for x in range(start,pos+1) ],lpath[start],lpath[pos],basename+str(count_name),chain_is_path=True)
count_name+=1
start=pos+1
checkStart=False
elif pos-start>1:
logging.debug("cutted DAG simplification (excluding the cutting node) at "+lpath[pos]+": ("+")->-(".join([ lpath[x] for x in range(start,pos) ] )+')')
chain_simplification([ lpath[x] for x in range(start,pos) ],lpath[start],lpath[pos-1],basename+str(count_name),chain_is_path=True)
count_name+=1
checkStart=True
else: checkStart=True
if checkStart:
start=pos
for e in list(g.out_edges(node)):
if not e[1] in nset:
start=pos+1
logging.debug("NB: Cutting node excluded as start")
break
if start==pos:
logging.debug("NB: Cutting node included as start")
if pos-start>0:
logging.debug("Final DAG simplification : ("+")->-(".join([ lpath[x] for x in range(start,pos+1) ] )+')')
chain_simplification([ lpath[x] for x in range(start,pos+1) ],lpath[start],lpath[pos],basename+str(count_name),chain_is_path=True)
count_name+=1
start=pos+1
"""
end of step 02 writing...
"""
logging.info("Write graph and fasta after DAG simplification")
mkdir_p(args.outDir+'/02-after_dag_graph')
nx.write_graphml(g, args.outDir+"/02-after_dag_graph/graph.graphml")
node2remove=list()
for node in g.nodes:
if nx.is_isolate(g,node): # WAY TO GET THE DEGREE OF NODE
#g.remove_node(node)
node2remove.append(node)
for node in node2remove:
g.remove_node(node)
with open(args.outDir+"/02-after_dag_graph/scaffolds.fasta", "w") as output_handle:
for key in seqDict.keys():
SeqIO.write(SeqRecord(seqDict[key],id=key,description=''), output_handle, "fasta")
