def work(self,
trace_set_paths,
conf,
keep_trace_sets=False,
keep_scores=True,
keep_ai=False):
"""
Actions to be performed by workers on the trace set given in trace_set_path.
"""
resolve_paths(trace_set_paths) # Get absolute paths
if type(trace_set_paths) is list:
result = EMResult(task_id=self.request.id) # Keep state and results
# Process trace set paths and fill in results of analysis
process_trace_set_paths(result,
trace_set_paths,
conf,
request_id=self.request.id,
keep_trace_sets=keep_trace_sets)
if not keep_trace_sets: # Do not return processed traces
result.trace_sets = None
result.reference_signal = None
if not keep_scores: # Do not return attack scores
result.correlations = None
result.distances = None
if not keep_ai:
result.ai = None # Do not return AI object
return result
else:
logger.error("Must provide a list of trace set paths to worker!")
return None
def merge(self, to_merge, conf):
if type(to_merge) is EMResult:
to_merge = [to_merge]
# Is it useful to merge?
if len(to_merge) >= 1:
result = EMResult(task_id=self.request.id)
# If we are attacking, merge the correlations
# TODO this can be cleaned up
if conf_has_op(conf, 'attack') or conf_has_op(
conf, 'memattack') or conf_has_op(conf, 'spattack'):
# Get size of correlations
shape = to_merge[
0].correlations._n.shape # TODO fixme init hetzelfde als in attack
# Init result
result.correlations = CorrelationList(shape)
# Start merging
for m in to_merge:
result.correlations.merge(m.correlations)
elif conf_has_op(
conf, 'dattack'
): # TODO just check for presence of to_merge.distances instead of doing this
shape = to_merge[0].distances._n.shape
result.distances = DistanceList(shape)
for m in to_merge:
result.distances.merge(m.distances)
elif conf_has_op(conf, 'pattack'):
shape = to_merge[0].probabilities.shape
result.probabilities = np.zeros(shape)
for m in to_merge:
result.probabilities += m.probabilities
elif conf_has_op(conf, 'keyplot'):
result.means = {}
tmp = defaultdict(lambda: [])
for m in to_merge:
for key, mean_traces in m.means.items():
tmp[key].extend(mean_traces)
for key, mean_traces in tmp.items():
all_traces = np.array(mean_traces)
print("Merging %d traces for subkey value %s" %
(all_traces.shape[0], key))
result.means[key] = np.mean(all_traces, axis=0)
# Clean up tasks
if conf.remote:
for m in to_merge:
logger.warning("Deleting %s" % m.task_id)
app.AsyncResult(m.task_id).forget()
return result
else:
return None