def respond_confirm_relation_from_literature(self, content):
"""Confirm a protein-protein interaction given subject, object, verb"""
try:
subj, obj, stmt_type, filter_agents = self._get_query_info(content)
finder = \
self.msa.find_mechanism_from_input(subj, obj, None, stmt_type,
ev_limit=5, persist=False,
timeout=5,
filter_agents=filter_agents)
self._send_provenance_async(finder,
'confirming that some statements match')
except MSALookupError as mle:
return self.make_failure(mle.args[0])
stmts = finder.get_statements(timeout=20)
if stmts is None:
# TODO: Handle this more gracefully, if possible.
return self.make_failure('MISSING_MECHANISM')
num_stmts = len(stmts)
description = finder.describe(include_negative=False) \
if stmts else None
#self.say(description)
resp = KQMLPerformative('SUCCESS')
resp.set('some-relations-found', 'TRUE' if num_stmts else 'FALSE')
resp.set('num-relations-found', str(num_stmts))
resp.set('dump-limit', str(DUMP_LIMIT))
resp.sets('suggestion', description if description else 'nil')
return resp
def respond_find_relations_from_literature(self, content):
"""Find statements matching some subject, verb, object information."""
try:
subj, obj, stmt_type, filter_agents = self._get_query_info(content)
finder = \
self.msa.find_mechanism_from_input(subj, obj, None, stmt_type,
ev_limit=3, persist=False,
timeout=5,
filter_agents=filter_agents)
self._send_provenance_async(finder,
'finding statements that match')
except MSALookupError as mle:
return self.make_failure(mle.args[0])
stmts = finder.get_statements(timeout=15)
if stmts is None:
# Calling this success may be a bit ambitious.
resp = KQMLPerformative('SUCCESS')
resp.set('status', 'WORKING')
resp.set('entities-found', 'nil')
resp.set('num-relations-found', '0')
resp.set('dump-limit', str(DUMP_LIMIT))
return resp
agents = finder.get_other_agents()
description = finder.describe(include_negative=False)
#self.say(description)
resp = KQMLPerformative('SUCCESS')
resp.set('status', 'FINISHED')
resp.set('entities-found', self.make_cljson(agents))
resp.set('num-relations-found', str(len(stmts)))
resp.set('dump-limit', str(DUMP_LIMIT))
resp.sets('suggestion', description)
resp.set('top-stmts', self.make_cljson(stmts[:10]))
return resp
def respond_phosphorylation_activating(self, content):
"""Return response content to phosphorylation_activating request."""
if not CAN_CHECK_STATEMENTS:
return self.make_failure(
'NO_KNOWLEDGE_ACCESS',
'Cannot access the database through the web api.')
heading = content.head()
m = re.match(r'(\w+)-(\w+)', heading)
if m is None:
return self.make_failure('UNKNOWN_ACTION')
action, polarity = [s.lower() for s in m.groups()]
target_cljson = content.get('target')
if target_cljson is None or not len(target_cljson):
return self.make_failure('MISSING_TARGET')
agent = self.get_agent(target_cljson)
# This is a bug in the BA that we can handle here
if isinstance(agent, list):
agent = agent[0]
logger.debug('Found agent (target): %s.' % agent.name)
site = content.gets('site')
if site is None:
residue = None
position = None
else:
try:
residue, position = site.split('-')
except:
return self.make_failure('INVALID_SITE')
finder = self.msa.find_phos_activeforms(agent,
residue=residue,
position=position,
action=action,
polarity=polarity)
stmts = finder.get_statements()
logger.info("Found %d matching statements." % len(stmts))
if not len(stmts):
return self.make_failure(
'MISSING_MECHANISM',
"Could not find statement matching phosphorylation activating "
"%s, %s, %s, %s." %
(agent.name, residue, position, 'phosphorylation'))
else:
description = finder.describe(include_negative=False)
# self.say(description)
msg = "phosphorylation at %s%s activates %s." \
% (residue, position, agent.name)
self.send_provenance_for_stmts(
stmts,
msg,
ev_counts=finder.get_ev_totals(),
source_counts=finder.get_source_counts())
msg = KQMLPerformative('SUCCESS')
msg.set('is-activating', 'TRUE')
msg.sets('suggestion', description)
return msg
def respond_build_model(self, content):
"""Return response content to build-model request."""
descr = content.gets('description')
descr_format = content.gets('format')
no_display = content.get('no-display')
if not descr_format or descr_format == 'ekb':
res = self.mra.build_model_from_ekb(descr)
elif descr_format == 'indra_json':
res = self.mra.build_model_from_json(descr)
else:
err_msg = 'Invalid description format: %s' % descr_format
raise InvalidModelDescriptionError(err_msg)
if res.get('error'):
raise InvalidModelDescriptionError(res.get('error'))
model_id = res.get('model_id')
if model_id is None:
raise InvalidModelDescriptionError()
# Start a SUCCESS message
msg = KQMLPerformative('SUCCESS')
# Add the model id
msg.set('model-id', str(model_id))
# Add the INDRA model json
model = res.get('model')
if model and (descr_format == 'ekb' or not descr_format):
self.send_background_support(model)
model_msg = encode_indra_stmts(model)
msg.sets('model', model_msg)
# Add the diagrams
diagrams = res.get('diagrams')
if not no_display:
if diagrams:
rxn_diagram = diagrams.get('reactionnetwork')
if rxn_diagram:
msg.sets('diagram', rxn_diagram)
self.send_display_model(diagrams)
# Indicate whether the goal has been explained
has_expl = res.get('has_explanation')
if has_expl is not None:
msg.set('has_explanation', str(has_expl).upper())
# Send out various model diagnosis messages
self.send_model_diagnoses(res)
# Once we sent out the diagnosis messages, we make sure we keep
# track of whether we have an explanation
if has_expl:
self.have_explanation = True
# Analyze the model for issues
# Report ambiguities
ambiguities = res.get('ambiguities')
if ambiguities:
ambiguities_msg = get_ambiguities_msg(ambiguities)
msg.set('ambiguities', ambiguities_msg)
return msg
def respond_get_common(self, content):
"""Find the common up/down streams of a protein."""
# TODO: This entire function could be part of the MSA.
if not CAN_CHECK_STATEMENTS:
return self.make_failure(
'NO_KNOWLEDGE_ACCESS',
'Cannot access the database through the web api.'
)
genes_ekb = content.gets('genes')
agents = _get_agents(genes_ekb)
if len(agents) < 2:
return self.make_failure('NO_TARGET',
'Only %d < 2 agents given.' % len(agents))
direction = content.gets('up-down')
logger.info("Got genes: %s and direction %s." % (agents, direction))
# Choose some parameters based on direction.
if direction == 'ONT::MORE':
method = 'common_upstreams'
prefix = 'up'
elif direction == 'ONT::SUCCESSOR':
method = 'common_downstreams'
prefix = 'down'
else:
# TODO: With the new MSA we could handle common neighbors.
return self.make_failure("UNKNOWN_ACTION", direction)
# Find the commonalities.
try:
finder = self.msa.find_mechanisms(method, *agents)
except EntityError as e:
return self.make_failure("MISSING_TARGET", e.args[0])
# Get post statements to provenance.
if len(agents) > 2:
name_list = ', '.join(ag.name for ag in agents[:-1]) + ','
else:
name_list = agents[0].name
name_list += ' and ' + agents[-1].name
msg = ('%sstreams of ' % prefix).capitalize() + name_list
self.send_provenance_for_stmts(finder.get_statements(), msg,
ev_counts=finder.get_ev_totals())
# Create the reply
resp = KQMLPerformative('SUCCESS')
gene_list = KQMLList()
for gene in finder.get_common_entities():
gene_list.append(gene)
resp.set('commons', gene_list)
resp.sets('prefix', prefix)
return resp
def respond_get_common(self, content):
"""Find the common up/down streams of a protein."""
# TODO: This entire function could be part of the MSA.
if not CAN_CHECK_STATEMENTS:
return self.make_failure(
'NO_KNOWLEDGE_ACCESS',
'Cannot access the database through the web api.')
genes_cljson = content.get('genes')
agents = [self.get_agent(ag) for ag in genes_cljson]
if len(agents) < 2:
return self.make_failure('NO_TARGET',
'Only %d < 2 agents given.' % len(agents))
direction = content.gets('up-down')
logger.info("Got genes: %s and direction %s." % (agents, direction))
# Choose some parameters based on direction.
if direction == 'ONT::MORE':
method = 'common_upstreams'
prefix = 'up'
elif direction == 'ONT::SUCCESSOR':
method = 'common_downstreams'
prefix = 'down'
else:
# TODO: With the new MSA we could handle common neighbors.
return self.make_failure("UNKNOWN_ACTION", direction)
# Find the commonalities.
try:
finder = self.msa.find_mechanisms(method, *agents)
except EntityError as e:
return self.make_failure("MISSING_TARGET", e.args[0])
# Get post statements to provenance.
if len(agents) > 2:
name_list = ', '.join(ag.name for ag in agents[:-1]) + ','
else:
name_list = agents[0].name
name_list += ' and ' + agents[-1].name
msg = ('%sstreams of ' % prefix).capitalize() + name_list
self.send_provenance_for_stmts(
finder.get_statements(),
msg,
ev_counts=finder.get_ev_totals(),
source_counts=finder.get_source_counts())
# Create the reply
resp = KQMLPerformative('SUCCESS')
agents = finder.get_other_agents()
resp.set('entities-found', self.make_cljson(agents))
resp.sets('prefix', prefix)
return resp
def register(self):
if self.name is not None:
perf = KQMLPerformative('register')
perf.set('name', self.name)
if self.group_name is not None:
try:
if self.group_name.startswith('('):
perf.sets('group', self.group_name)
else:
perf.set('group', self.group_name)
except IOError:
logger.error('bad group name: ' + self.group_name)
self.send(perf)
def register(self):
if self.name is not None:
perf = KQMLPerformative('register')
perf.set('name', self.name)
if self.group_name is not None:
try:
if self.group_name.startswith('('):
perf.sets('group', self.group_name)
else:
perf.set('group', self.group_name)
except IOError:
logger.error('bad group name: ' + self.group_name)
self.send(perf)
def respond_model_remove_mechanism(self, content):
"""Return response content to model-remove-mechanism request."""
model_id = self._get_model_id(content)
descr = content.get('description')
js = json.dumps(self.converter.cl_to_json(descr))
no_display = content.get('no-display')
try:
res = self.mra.remove_mechanism(js, model_id)
except Exception as e:
raise InvalidModelDescriptionError(e)
model_id = res.get('model_id')
if model_id is None:
raise InvalidModelDescriptionError('Could not find model id.')
# Start a SUCCESS message
msg = KQMLPerformative('SUCCESS')
# Add the model id
msg.set('model-id', str(model_id))
# Add the INDRA model json
model = res.get('model')
model_msg = encode_indra_stmts(model)
msg.set('model', model_msg)
# Handle empty model
if not model:
self.send_clean_model()
# Get the action and add it to the message
removed = res.get('removed')
if not removed:
msg = self.make_failure('REMOVE_FAILED')
return msg
else:
actionl = KQMLList('remove_stmts')
actionl.set('statements', encode_indra_stmts(removed))
msg.set('action', actionl)
# Add the diagram
diagrams = res.get('diagrams')
logger.info(diagrams)
if not no_display:
if diagrams:
rxn_diagram = diagrams.get('reactionnetwork')
if rxn_diagram:
msg.sets('diagram', rxn_diagram)
self.send_display_model(diagrams)
return msg
def respond_model_remove_mechanism(self, content):
"""Return response content to model-remove-mechanism request."""
ekb = content.gets('description')
model_id = self._get_model_id(content)
no_display = content.get('no-display')
try:
res = self.mra.remove_mechanism(ekb, model_id)
except Exception as e:
raise InvalidModelDescriptionError(e)
model_id = res.get('model_id')
if model_id is None:
raise InvalidModelDescriptionError('Could not find model id.')
# Start a SUCCESS message
msg = KQMLPerformative('SUCCESS')
# Add the model id
msg.set('model-id', str(model_id))
# Add the INDRA model json
model = res.get('model')
model_msg = encode_indra_stmts(model)
msg.sets('model', model_msg)
# Handle empty model
if not model:
self.send_clean_model()
# Get the action and add it to the message
removed = res.get('removed')
if not removed:
msg = self.make_failure('REMOVE_FAILED')
return msg
else:
actionl = KQMLList('remove_stmts')
actionl.sets('statements', encode_indra_stmts(removed))
msg.set('action', actionl)
# Add the diagram
diagrams = res.get('diagrams')
logger.info(diagrams)
if not no_display:
if diagrams:
rxn_diagram = diagrams.get('reactionnetwork')
if rxn_diagram:
msg.sets('diagram', rxn_diagram)
self.send_display_model(diagrams)
return msg
def respond_model_has_mechanism(self, content):
"""Return response content to model-has-mechanism request."""
ekb = content.gets('description')
model_id = self._get_model_id(content)
try:
res = self.mra.has_mechanism(ekb, model_id)
except Exception as e:
raise InvalidModelDescriptionError(e)
# Start a SUCCESS message
msg = KQMLPerformative('SUCCESS')
# Add the model id
msg.set('model-id', str(model_id))
# Add TRUE or FALSE for has-mechanism
has_mechanism_msg = 'TRUE' if res['has_mechanism'] else 'FALSE'
msg.set('has-mechanism', has_mechanism_msg)
query = res.get('query')
if query:
query_msg = encode_indra_stmts([query])
msg.sets('query', query_msg)
return msg
def respond_has_mechanism(self, content):
"""Return response content to model-has-mechanism request."""
ekb = content.gets('description')
model_id = self._get_model_id(content)
try:
res = self.mra.has_mechanism(ekb, model_id)
except Exception as e:
raise InvalidModelDescriptionError(e)
# Start a SUCCESS message
msg = KQMLPerformative('SUCCESS')
# Add the model id
msg.set('model-id', str(model_id))
# Add TRUE or FALSE for has-mechanism
has_mechanism_msg = 'TRUE' if res['has_mechanism'] else 'FALSE'
msg.set('has-mechanism', has_mechanism_msg)
query = res.get('query')
if query:
query_msg = encode_indra_stmts([query])
msg.sets('query', query_msg)
return msg
def respond_build_model(self, content):
"""Return response content to build-model request."""
descr = content.gets('description')
descr_format = content.gets('format')
no_display = content.get('no-display')
if not descr_format or descr_format == 'ekb':
res = self.mra.build_model_from_ekb(descr)
elif descr_format == 'indra_json':
res = self.mra.build_model_from_json(descr)
else:
err_msg = 'Invalid description format: %s' % descr_format
raise InvalidModelDescriptionError(err_msg)
if res.get('error'):
raise InvalidModelDescriptionError(res.get('error'))
model_id = res.get('model_id')
if model_id is None:
raise InvalidModelDescriptionError()
# Start a SUCCESS message
msg = KQMLPerformative('SUCCESS')
# Add the model id
msg.set('model-id', str(model_id))
# Add the INDRA model json
model = res.get('model')
if model:
self.send_background_support(model)
model_msg = encode_indra_stmts(model)
msg.sets('model', model_msg)
# Add the diagrams
diagrams = res.get('diagrams')
if not no_display:
if diagrams:
rxn_diagram = diagrams.get('reactionnetwork')
if rxn_diagram:
msg.sets('diagram', rxn_diagram)
self.send_display_model(diagrams)
ambiguities = res.get('ambiguities')
if ambiguities:
ambiguities_msg = get_ambiguities_msg(ambiguities)
msg.set('ambiguities', ambiguities_msg)
return msg
def respond_remove_mechanism(self, content):
"""Return response content to model-remove-mechanism request."""
ekb = content.gets('description')
model_id = self._get_model_id(content)
no_display = content.get('no-display')
try:
res = self.mra.remove_mechanism(ekb, model_id)
except Exception as e:
raise InvalidModelDescriptionError(e)
model_id = res.get('model_id')
if model_id is None:
raise InvalidModelDescriptionError('Could not find model id.')
# Start a SUCCESS message
msg = KQMLPerformative('SUCCESS')
# Add the model id
msg.set('model-id', str(model_id))
# Add the INDRA model json
model = res.get('model')
model_msg = encode_indra_stmts(model)
msg.sets('model', model_msg)
# Add the removed statements
removed = res.get('removed')
if removed:
removed_msg = encode_indra_stmts(removed)
msg.sets('removed', removed_msg)
# Add the diagram
diagrams = res.get('diagrams')
if not no_display:
if diagrams:
rxn_diagram = diagrams.get('reactionnetwork')
if rxn_diagram:
msg.sets('diagram', rxn_diagram)
self.send_display_model(rxn_diagram)
return msg
def respond_model_undo(self, content):
"""Return response content to model-undo request."""
res = self.mra.model_undo()
no_display = content.get('no-display')
model_id = res.get('model_id')
# Start a SUCCESS message
msg = KQMLPerformative('SUCCESS')
# Add the model id
msg.set('model-id', 'NIL' if model_id is None else str(model_id))
# Add the INDRA model json
model = res.get('model')
# Handle empty model
if not model:
self.send_clean_model()
model_msg = encode_indra_stmts(model)
msg.sets('model', model_msg)
# Get the action and add it to the message
action = res.get('action')
actionl = KQMLList()
# Here we handle no action as effectively an empty remove action
if action['action'] in ('no_op', 'remove_stmts'):
actionl.append('remove_stmts')
actionl.sets(
'statements',
encode_indra_stmts(action['statements'])
)
msg.set('action', actionl)
# Add the diagram
diagrams = res.get('diagrams')
if not no_display:
if diagrams:
rxn_diagram = diagrams.get('reactionnetwork')
if rxn_diagram:
msg.sets('diagram', rxn_diagram)
self.send_display_model(diagrams)
return msg
def respond_model_undo(self, content):
"""Return response content to model-undo request."""
res = self.mra.model_undo()
no_display = content.get('no-display')
model_id = res.get('model_id')
# Start a SUCCESS message
msg = KQMLPerformative('SUCCESS')
# Add the model id
msg.set('model-id', 'NIL' if model_id is None else str(model_id))
# Add the INDRA model json
model = res.get('model')
# Handle empty model
if not model:
self.send_clean_model()
model_msg = encode_indra_stmts(model)
msg.set('model', model_msg)
# Get the action and add it to the message
action = res.get('action')
actionl = KQMLList()
# Here we handle no action as effectively an empty remove action
if action['action'] in ('no_op', 'remove_stmts'):
actionl.append('remove_stmts')
actionl.set(
'statements',
encode_indra_stmts(action['statements'])
)
msg.set('action', actionl)
# Add the diagram
diagrams = res.get('diagrams')
if not no_display:
if diagrams:
rxn_diagram = diagrams.get('reactionnetwork')
if rxn_diagram:
msg.sets('diagram', rxn_diagram)
self.send_display_model(diagrams)
return msg
def respond_choose_sense(self, content):
"""Return response content to choose-sense request."""
agent_clj = content.get('agent')
agent = self.get_agent(agent_clj)
if not agent:
return self.make_failure('MISSING_AGENT')
add_agent_type(agent)
def _get_urls(agent):
urls = {
k: get_identifiers_url(k, v)
for k, v in agent.db_refs.items()
if k not in {'TEXT', 'TYPE', 'TRIPS'}
}
return urls
msg = KQMLPerformative('SUCCESS')
msg.set('agent', self.make_cljson(agent))
description = None
if 'UP' in agent.db_refs:
description = uniprot_client.get_function(agent.db_refs['UP'])
if description:
msg.sets('description', description)
urls = _get_urls(agent)
if urls:
url_parts = [
KQMLList([':name',
KQMLString(k), ':dblink',
KQMLString(v)]) for k, v in urls.items()
]
url_list = KQMLList()
for url_part in url_parts:
url_list.append(url_part)
msg.set('id-urls', url_list)
return msg
def respond_model_undo(self, content):
"""Return response content to model-undo request."""
res = self.mra.model_undo()
no_display = content.get('no-display')
new_model_id = res.get('model_id')
# Start a SUCCESS message
msg = KQMLPerformative('SUCCESS')
# Add the model id
msg.set('model-id', str(new_model_id))
# Add the INDRA model json
model = res.get('model')
model_msg = encode_indra_stmts(model)
msg.sets('model', model_msg)
# Add the diagram
diagrams = res.get('diagrams')
if not no_display:
if diagrams:
rxn_diagram = diagrams.get('reactionnetwork')
if rxn_diagram:
msg.sets('diagram', rxn_diagram)
if not self.testing:
self.send_display_model(model_msg, diagrams)
return msg
def respond_expand_model(self, content):
"""Return response content to expand-model request."""
descr = content.gets('description')
model_id = self._get_model_id(content)
descr_format = content.gets('format')
no_display = content.get('no-display')
try:
if not descr_format or descr_format == 'ekb':
res = self.mra.expand_model_from_ekb(descr, model_id)
elif descr_format == 'indra_json':
res = self.mra.expand_model_from_json(descr, model_id)
else:
err_msg = 'Invalid description format: %s' % descr_format
raise InvalidModelDescriptionError(err_msg)
except Exception as e:
raise InvalidModelDescriptionError(e)
new_model_id = res.get('model_id')
if new_model_id is None:
raise InvalidModelDescriptionError()
# Start a SUCCESS message
msg = KQMLPerformative('SUCCESS')
# Add the model id
msg.set('model-id', str(new_model_id))
# Add the INDRA model json
model = res.get('model')
model_msg = encode_indra_stmts(model)
msg.sets('model', model_msg)
# Add the INDRA model new json
model_new = res.get('model_new')
if model_new:
model_new_msg = encode_indra_stmts(model_new)
msg.sets('model-new', model_new_msg)
# Add the diagram
if not no_display:
diagrams = res.get('diagrams')
if diagrams:
rxn_diagram = diagrams.get('reactionnetwork')
if rxn_diagram:
msg.sets('diagram', rxn_diagram)
if not self.testing:
self.send_display_model(model_msg, diagrams)
ambiguities = res.get('ambiguities')
if ambiguities:
ambiguities_msg = get_ambiguities_msg(ambiguities)
msg.set('ambiguities', ambiguities_msg)
return msg
def respond_expand_model(self, content):
"""Return response content to expand-model request."""
model_id = self._get_model_id(content)
descr_format = content.gets('format')
no_display = content.get('no-display')
try:
if not descr_format:
descr = content.get('description')
js = json.dumps(self.converter.cl_to_json(descr))
res = self.mra.expand_model_from_json(js, model_id)
elif descr_format == 'ekb':
descr = content.gets('description')
res = self.mra.expand_model_from_ekb(descr, model_id)
elif descr_format == 'indra_json':
descr = content.gets('description')
res = self.mra.expand_model_from_json(descr, model_id)
else:
err_msg = 'Invalid description format: %s' % descr_format
raise InvalidModelDescriptionError(err_msg)
except Exception as e:
raise InvalidModelDescriptionError(e)
new_model_id = res.get('model_id')
if new_model_id is None:
raise InvalidModelDescriptionError()
# Start a SUCCESS message
msg = KQMLPerformative('SUCCESS')
# Add the model id
msg.set('model-id', str(new_model_id))
# Add the INDRA model json
model = res.get('model')
model_msg = encode_indra_stmts(model)
msg.set('model', model_msg)
# Add the INDRA model new json
model_new = res.get('model_new')
# Indicate whether the goal has been explained
has_expl = res.get('has_explanation')
if has_expl is not None:
msg.set('has_explanation', str(has_expl).upper())
# Send out various model diagnosis messages
diagnostic_tells = self.send_model_diagnoses(res)
if diagnostic_tells:
suggs = KQMLList()
for text in diagnostic_tells:
sugg = KQMLList()
sugg.sets('TEXT', text)
suggs.append(sugg)
msg.set('suggestions', suggs)
# Once we sent out the diagnosis messages, we make sure we keep
# track of whether we have an explanation
if has_expl:
self.have_explanation = True
if model_new and (descr_format == 'ekb' or not descr_format):
self.send_background_support(model_new)
if model_new:
model_new_msg = encode_indra_stmts(model_new)
msg.set('model-new', model_new_msg)
# Add the diagram
if not no_display:
diagrams = res.get('diagrams')
if diagrams:
rxn_diagram = diagrams.get('reactionnetwork')
if rxn_diagram:
msg.sets('diagram', rxn_diagram)
self.send_display_model(diagrams)
# Analyze the model for issues
# Report ambiguities
ambiguities = res.get('ambiguities')
if ambiguities:
ambiguities_msg = get_ambiguities_msg(ambiguities)
msg.set('ambiguities', ambiguities_msg)
return msg
def respond_get_indra_representation(self, content):
"""Return the INDRA CL-JSON corresponding to the given content."""
# First get the KQML graph object for the given context
context = content.get('context').to_string()
graph = KQMLGraph(context)
# Next, we look at each of the IDs that we need to build EKBs for
# and build the EKBs one by one
ekbs = []
for trips_id_obj in content.get('ids'):
trips_id = trips_id_obj.to_string()
if trips_id.startswith('ONT::'):
trips_id = trips_id[5:]
try:
# Turn the graph into an EKB XML object, expanding around
# the given ID.
ekb = EKB(graph, trips_id)
logger.debug('Extracted EKB: %s' % ekb.to_string())
ekbs.append((trips_id, ekb))
except Exception as e:
logger.error('Encountered an error while parsing: %s.' %
content.to_string())
logger.exception(e)
msg = KQMLPerformative('done')
# If we didn't get any EKBs, we just return an empty result
if not ekbs:
msg.set('result', KQMLList())
return msg
# If there is one EKB then we work with that
elif len(ekbs) == 1:
ekbs_to_extract = ekbs
# If there are multiple EKBs, we need to make sure that we are not
# extracting IDs passed for embedded events (whose root ID, i.e.,
# trips_id is somewhere inside another EKB already). We therefore
# iterate over all the EKBs and check this condition. If the EKB
# isn't subsumed, it's added to a list of ones to be extracted.
else:
ekbs_to_extract = []
for idx, (trips_id, ekb) in enumerate(ekbs):
other_ekbs = ekbs[:idx] + ekbs[idx + 1:]
other_components = set.union(
*[set(e.components) for _, e in other_ekbs])
if trips_id in other_components:
continue
ekbs_to_extract.append((trips_id, ekb))
# Finally, we extract entities (Agents or Statements) from the given
# EKB, and add each extraction to a list
entities = KQMLList()
for trips_id, ekb in ekbs_to_extract:
entity = ekb.get_entity()
if entity is None:
logger.info("Could not resolve entity from: %s. " %
ekb.to_string())
else:
js = self.make_cljson(entity)
entities.append(js)
if len(entities) == 1:
msg.sets('result', entities[0])
else:
msg.set('result', entities)
return msg
def error_reply(self, msg, comment):
reply_msg = KQMLPerformative('error')
reply_msg.sets('comment', comment)
self.reply(msg, reply_msg)
def error_reply(self, msg, comment):
reply_msg = KQMLPerformative('error')
reply_msg.sets('comment', comment)
self.reply(msg, reply_msg)
def respond_build_model(self, content):
"""Return response content to build-model request."""
descr_format = content.gets('format')
if descr_format:
logger.info('Building model from format: %s' % descr_format)
no_display = content.get('no-display')
if not descr_format:
descr = content.get('description')
js_data = self.converter.cl_to_json(descr)
if isinstance(js_data, dict):
logger.error('JSON data should be a list not a dict.')
raise InvalidModelDescriptionError("Model description should "
"be a list of events.")
js = json.dumps(js_data)
res = self.mra.build_model_from_json(js)
elif descr_format == 'ekb':
descr = content.gets('description')
res = self.mra.build_model_from_ekb(descr)
elif descr_format == 'indra_json':
descr = content.gets('description')
res = self.mra.build_model_from_json(descr)
else:
err_msg = 'Invalid description format: %s' % descr_format
raise InvalidModelDescriptionError(err_msg)
if res.get('error'):
raise InvalidModelDescriptionError(res.get('error'))
model_id = res.get('model_id')
if model_id is None:
raise InvalidModelDescriptionError()
# Start a SUCCESS message
msg = KQMLPerformative('SUCCESS')
# Add the model id
msg.set('model-id', str(model_id))
# Add the INDRA model json
model = res.get('model')
if model and (descr_format == 'ekb' or not descr_format):
self.send_background_support(model)
model_msg = encode_indra_stmts(model)
msg.set('model', model_msg)
# Add the diagrams
diagrams = res.get('diagrams')
if not no_display:
if diagrams:
rxn_diagram = diagrams.get('reactionnetwork')
if rxn_diagram:
msg.sets('diagram', rxn_diagram)
self.send_display_model(diagrams)
# Indicate whether the goal has been explained
has_expl = res.get('has_explanation')
if has_expl is not None:
msg.set('has_explanation', str(has_expl).upper())
# Send out various model diagnosis messages
diagnostic_tells = self.send_model_diagnoses(res)
if diagnostic_tells:
suggs = KQMLList()
for text in diagnostic_tells:
sugg = KQMLList()
sugg.sets('TEXT', text)
suggs.append(sugg)
msg.set('suggestions', suggs)
# Once we sent out the diagnosis messages, we make sure we keep
# track of whether we have an explanation
if has_expl:
self.have_explanation = True
# Analyze the model for issues
# Report ambiguities
ambiguities = res.get('ambiguities')
if ambiguities:
ambiguities_msg = get_ambiguities_msg(ambiguities)
msg.set('ambiguities', ambiguities_msg)
return msg