def global_message():
"""Parse the decision tree and return the message
corresponding to the current threasholds values"""
# Compute the weight for each item in the tree
current_thresholds = glances_thresholds.get()
for i in tree:
i['weight'] = sum([current_thresholds[t].value() for t in i['thresholds'] if t in current_thresholds])
themax = max(tree, key=lambda d: d['weight'])
if themax['weight'] >= themax['thresholds_min']:
# Check if the weight is > to the minimal threashold value
return themax['msg']
else:
return tree[0]['msg']
def global_message():
"""Parse the decision tree and return the message.
Note: message corresponding to the current threasholds values
"""
# Compute the weight for each item in the tree
current_thresholds = glances_thresholds.get()
for i in tree:
i['weight'] = sum([current_thresholds[t].value() for t in i['thresholds'] if t in current_thresholds])
themax = max(tree, key=lambda d: d['weight'])
if themax['weight'] >= themax['thresholds_min']:
# Check if the weight is > to the minimal threashold value
return themax['msg']
else:
return tree[0]['msg']