# -*- coding: utf-8 -*-
import logging
from .constants import *
from ..mutation import (
expand_nodes_neighborhoods, get_multi_causal_downstream, get_multi_causal_upstream,
get_random_subgraph, get_subgraph_by_all_shortest_paths, get_subgraph_by_annotations, get_subgraph_by_authors,
get_subgraph_by_induction, get_subgraph_by_neighborhood, get_subgraph_by_pubmed, get_subgraph_by_second_neighbors,
)
log = logging.getLogger(__name__)
__all__ = [
'get_subgraph',
]
[docs]def get_subgraph(graph, seed_method=None, seed_data=None, expand_nodes=None, remove_nodes=None):
"""Run a pipeline query on graph with multiple sub-graph filters and expanders.
Order of Operations:
1. Seeding by given function name and data
2. Add nodes
3. Remove nodes
:param pybel.BELGraph graph: A BEL graph
:param str seed_method: The name of the get_subgraph_by_* function to use
:param seed_data: The argument to pass to the get_subgraph function
:param list[tuple] expand_nodes: Add the neighborhoods around all of these nodes
:param list[tuple] remove_nodes: Remove these nodes and all of their in/out edges
:rtype: Optional[pybel.BELGraph]
"""
# Seed by the given function
if seed_method == SEED_TYPE_INDUCTION:
result = get_subgraph_by_induction(graph, seed_data)
elif seed_method == SEED_TYPE_PATHS:
result = get_subgraph_by_all_shortest_paths(graph, seed_data)
elif seed_method == SEED_TYPE_NEIGHBORS:
result = get_subgraph_by_neighborhood(graph, seed_data)
elif seed_method == SEED_TYPE_DOUBLE_NEIGHBORS:
result = get_subgraph_by_second_neighbors(graph, seed_data)
elif seed_method == SEED_TYPE_UPSTREAM:
result = get_multi_causal_upstream(graph, seed_data)
elif seed_method == SEED_TYPE_DOWNSTREAM:
result = get_multi_causal_downstream(graph, seed_data)
elif seed_method == SEED_TYPE_PUBMED:
result = get_subgraph_by_pubmed(graph, seed_data)
elif seed_method == SEED_TYPE_AUTHOR:
result = get_subgraph_by_authors(graph, seed_data)
elif seed_method == SEED_TYPE_ANNOTATION:
result = get_subgraph_by_annotations(graph, seed_data['annotations'], or_=seed_data.get('or'))
elif seed_method == SEED_TYPE_SAMPLE:
result = get_random_subgraph(
graph,
number_edges=seed_data.get('number_edges'),
seed=seed_data.get('seed')
)
elif not seed_method: # Otherwise, don't seed a sub-graph
result = graph.copy()
log.debug('no seed function - using full network: %s', result.name)
else:
raise ValueError('Invalid seed method: {}'.format(seed_method))
if result is None:
log.debug('query returned no results')
return
log.debug('original graph has (%s nodes / %s edges)', result.number_of_nodes(), result.number_of_edges())
# Expand around the given nodes
if expand_nodes:
expand_nodes_neighborhoods(graph, result, expand_nodes)
log.debug('graph expanded to (%s nodes / %s edges)', result.number_of_nodes(), result.number_of_edges())
# Delete the given nodes
if remove_nodes:
for node in remove_nodes:
if node not in result:
log.debug('%s is not in graph %s', node, graph.name)
continue
result.remove_node(node)
log.debug('graph contracted to (%s nodes / %s edges)', result.number_of_nodes(), result.number_of_edges())
log.debug(
'Subgraph coming from %s (seed type) %s (data) contains %d nodes and %d edges',
seed_method,
seed_data,
result.number_of_nodes(),
result.number_of_edges()
)
return result