def metric_raw(self, results, formula):
#nmetric = results[1]
metrics, _ = results
# Build points dictionnary
points = {}
length = False
for m in metrics:
cid = m['meta']['data_id']
mid = 'metric_' + hashlib.md5(cid).hexdigest()
mname = self.retreive_metric_name(cid)
# Replace metric name in formula by the unique id
formula = formula.replace(mname, mid)
self.logger.debug("Metric {0} - {1}".format(mname, mid))
points[mid] = m['points']
# Make sure we treat the same amount of points by selecting
# The metric with less points.
if not length or len(m['points']) < length:
length = len(m['points'])
self.logger.debug('formula: {}'.format(formula))
#self.logger.debug('points: {}'.format(points))
mids = points.keys()
finalSerie = []
# Now loop over all points to calculate the final serie
for i in range(length):
data = {}
ts = 0
for j in range(len(mids)):
mid = mids[j]
# Get point value at timestamp "i" for metric "mid"
data[mid] = points[mid][i][1]
# Set timestamp
ts = points[mid][i][0]
# import data in math context
math = Formulas(data)
# Evaluate the mathematic formula
pointval = math.evaluate(formula)
# Add computed point in the serie
finalSerie.append([ts * 1000, pointval])
# Remove variables values from math context
math.reset()
self.logger.debug('finalserie: {}'.format(finalSerie))
return finalSerie, points[mid]
