def score_all(functionNode):
"""
score all thresholds again by using the stream implementation
#works only on context of the class object
"""
logger = functionNode.get_logger()
logger.debug("score_all")
progressNode = functionNode.get_child("control").get_child("progress")
progressNode.set_value(0)
model = functionNode.get_model() # for the model API
annos = functionNode.get_child("annotations").get_leaves()
annos = [
anno for anno in annos if anno.get_child("type").get_value() == "time"
] #only the time annotations
variableIds = functionNode.get_child(
"variables").get_leaves_ids() # the variableids to work on
try:
overWrite = functionNode.get_child("overWrite").get_value()
except:
overWrite = True
obj = functionNode.get_parent().get_object()
obj.reset(
) #read the new thresholds into the object!! this also affects parallel streaming processes
# for each id (variable) that has threshold(s)
# take the values and times of that varialbe
# find out the annotations we need, create the stream data blob, send it over
progressStep = 1 / float(len(obj.get_thresholds()))
total = None
for id, thresholdsInfo in obj.get_thresholds().items(
): # thresholds is a dict of {id: {tag:{"min":0,"max":1}, tag2:{} .. ,id2:{}}
if id not in variableIds:
continue # skip this one, is not selected
progressNode.set_value(progressNode.get_value() + progressStep)
var = model.get_node(id)
data = var.get_time_series()
times = data["__time"]
#now produce the interesting states
blob = {
"type": "timeseries",
"data": {
"__time": times,
id: data["values"],
"__states": {}
}
}
for state in thresholdsInfo.keys(
): #iterate over the states where the variable has special thresholds
myAnnos = mh.filter_annotations(annos, state)
stateMask = mh.annotations_to_class_vector(myAnnos, data["__time"])
stateMask = numpy.isfinite(stateMask)
blob["data"]["__states"][state] = stateMask
#now we have prepared a data and state blob, we will now score by feeding it into the stream scorer
#del blob["data"]["__states"]#for test, now
blob = obj.feed(blob)
#now the blob contains more entries, e.g. the score variable id and the according scores, that is what we want
for blobId, values in blob["data"].items():
if blobId not in ["__time", id, "__states"]:
#this is the score, overwrite the whole thing
scoreNode = model.get_node(blobId)
if scoreNode.get_name() == "_total_score":
continue # this is the combined result of several variables going into the stream scoring, not relevant here
scoreNode.set_time_series(
values=values, times=times
) # xxx is set ok here, or do we need "insert" to make sure there has not been changed in the meantime?
model.notify_observers(scoreNode.get_parent().get_id(),
"children") # we trigger
# build the total score:
# merge in the new times, resample the total score, resampel the local score, then merge them
# the merge function will use the new values whereever there is one (empty fields are named "nan"
# for the total score, we need a resampling to avoid the mixing of results e.g.
# two sensor have different result during a given interval, but different times, if we just merge
# we get a True, False, True,False mixture
# so we build the merge vector, first resample then merge
values[numpy.isfinite(
values)] = -1 # set -1 for all out of limit
if type(total) is type(None):
total = TimeSeries(values=values, times=times)
else:
local = TimeSeries(values=values, times=times)
total.merge(
local
) # the merge resamples the incoming data to the existing time series, NaN will be replaced by new values,
# finally, write the total
# if the overWrite is True, we replace, otherwise we merge with the existing, previous result
totalScoreNode = functionNode.get_parent().get_child("output").get_child(
"_total_score")
if overWrite:
totalScoreNode.set_time_series(values=total.get_values(),
times=total.get_times())
else:
totalScoreNode.merge_time_series(values=total.get_values(),
times=total.get_times())
return True
