def stmts_from_indranet_path(path, model, signed, from_db=True, stmts=None):
"""Return source Statements corresponding to a path in an IndraNet model
(found by SignedGraphModelChecker or UnsignedGraphModelChecker).
Parameters
----------
path : list[tuple[str, int]]
A list of tuples where the first element of the tuple is the
name of an agent, and the second is the associated polarity along
a path.
model : nx.Digraph or nx.MultiDiGraph
An IndraNet model flattened into an unsigned DiGraph or signed
MultiDiGraph.
signed : bool
Whether the model and path are signed.
from_db : bool
If True, uses statement hashes to query the database. Otherwise, looks
for path statements in provided stmts.
stmts : Optional[list[indra.statements.Statement]]
A list of INDRA Statements from which the model was assembled. Required
if from_db is set to False.
Returns
-------
path_stmts : list[[indra.statements.Statement]]
A list of lists of INDRA statements explaining the path (each inner
corresponds to one step in the path because the flattened model can
have multiple statements per edge).
"""
steps = []
for i in range(len(path[:-1])):
source = path[i]
target = path[i+1]
if len(source) == 3:
edge = RefEdge._from_json(source)
steps.append([edge])
continue
elif len(target) == 3:
edge = RefEdge._from_json(target)
steps.append([edge])
continue
if signed:
if source[1] == target[1]:
sign = 0
else:
sign = 1
stmt_data = model[source[0]][target[0]][sign]['statements']
else:
stmt_data = model[source[0]][target[0]]['statements']
hashes = [stmt['stmt_hash'] for stmt in stmt_data]
if from_db:
p = get_statements_by_hash(hashes)
statements = p.statements
else:
statements = [
stmt for stmt in stmts if stmt.get_hash() in hashes]
steps.append(statements)
return steps
def stmts_from_pybel_path(path, model, from_db=True, stmts=None):
"""Return source Statements corresponding to a path in a PyBEL model.
Parameters
----------
path : list[tuple[str, int]]
A list of tuples where the first element of the tuple is the
name of an agent, and the second is the associated polarity along
a path.
model : pybel.BELGraph
A PyBEL BELGraph model.
from_db : bool
If True, uses statement hashes to query the database. Otherwise, looks
for path statements in provided stmts.
stmts : Optional[list[indra.statements.Statement]]
A list of INDRA Statements from which the model was assembled. Required
if from_db is set to False.
Returns
-------
path_stmts : list[[indra.statements.Statement]]
A list of lists of INDRA statements explaining the path (each inner
corresponds to one step in the path because PyBEL model can have
multiple edges representing multiple statements and evidences between
two nodes).
"""
steps = []
for i in range(len(path[:-1])):
source = path[i]
target = path[i+1]
edges = model[source[0]][target[0]]
hashes = set()
for j in range(len(edges)):
hashes.add(edges[j]['stmt_hash'])
if from_db:
statements = get_statements_by_hash(list(hashes))
else:
statements = [
stmt for stmt in stmts if stmt.get_hash() in hashes]
steps.append(statements)
return steps
def stmts_from_pybel_path(path, model, from_db=True, stmts=None):
"""Return source Statements corresponding to a path in a PyBEL model.
Parameters
----------
path : list[tuple[str, int]]
A list of tuples where the first element of the tuple is the
name of an agent, and the second is the associated polarity along
a path.
model : pybel.BELGraph
A PyBEL BELGraph model.
from_db : bool
If True, uses statement hashes to query the database. Otherwise, looks
for path statements in provided stmts.
stmts : Optional[list[indra.statements.Statement]]
A list of INDRA Statements from which the model was assembled. Required
if from_db is set to False.
Returns
-------
path_stmts : list[[indra.statements.Statement]]
A list of lists of INDRA statements explaining the path (each inner
corresponds to one step in the path because PyBEL model can have
multiple edges representing multiple statements and evidences between
two nodes).
"""
import pybel.constants as pc
from indra.sources.bel.processor import get_agent
steps = []
for i in range(len(path[:-1])):
source = path[i]
target = path[i + 1]
# Check if the signs of source and target nodes are the same
positive = (source[1] == target[1])
reverse = False
try:
all_edges = model[source[0]][target[0]]
except KeyError:
# May be a symmetric edge
all_edges = model[target[0]][source[0]]
reverse = True
# Only keep the edges with correct sign or non-causal
edges = {}
key = 0
for edge_data in all_edges.values():
if edge_data['relation'] not in pc.CAUSAL_RELATIONS:
edges[key] = edge_data
key += 1
if positive and \
edge_data['relation'] in pc.CAUSAL_INCREASE_RELATIONS:
edges[key] = edge_data
key += 1
elif not positive and \
edge_data['relation'] in pc.CAUSAL_DECREASE_RELATIONS:
edges[key] = edge_data
key += 1
else:
continue
hashes = set()
for j in range(len(edges)):
try:
hashes.add(edges[j]['annotations']['stmt_hash'])
# partOf and hasVariant edges don't have hashes
except KeyError:
continue
# If we didn't get any hashes, we can get PybelEdge object from
# partOf and hasVariant edges
if not hashes:
statements = []
# Can't get statements without hash from db
for edge_v in edges.values():
rel = edge_v['relation']
edge = PybelEdge(get_agent(source[0]), get_agent(target[0]),
rel, reverse)
statements.append(edge)
# Stop if we have an edge to avoid duplicates
if len(statements) > 0:
break
# If we have hashes, retrieve statements from them
else:
if from_db:
statements = get_statements_by_hash(list(hashes),
simple_response=True)
else:
statements = [
stmt for stmt in stmts if stmt.get_hash() in hashes
]
steps.append(statements)
return steps