def evaluateTokens(tokens, timeInterval, originalTime = None):
if tokens.expression:
if tokens[0][0][0] == "timeShift":
delta = timeInterval[0] - parseATTime(tokens[0][0][1][1]['string'].strip('"'))
delta += timeInterval[1] - timeInterval[0]
originalTime = timeInterval
timeInterval = (timeInterval[0] - delta, timeInterval[1] - delta)
return evaluateTokens(tokens.expression, timeInterval, originalTime)
elif tokens.pathExpression:
pathExpr = tokens.pathExpression
if pathExpr.lower().startswith('graphite.'):
pathExpr = pathExpr[9:]
seriesList = []
(startTime,endTime) = originalTime or timeInterval
for dbFile in settings.STORE.find(pathExpr):
log.metric_access(dbFile.metric_path)
getCacheResults = CarbonLink.sendRequest(dbFile.real_metric)
dbResults = dbFile.fetch( timestamp(startTime), timestamp(endTime) )
results = mergeResults(dbResults, getCacheResults())
if not results:
continue
(timeInfo,values) = results
(start,end,step) = timeInfo
series = TimeSeries(dbFile.metric_path, start, end, step, values)
series.pathExpression = pathExpr #hack to pass expressions through to render functions
series.start = time.mktime(startTime.timetuple())
series.end = time.mktime(endTime.timetuple())
seriesList.append(series)
return seriesList
elif tokens.call:
func = SeriesFunctions[tokens.call.func]
args = [evaluateTokens(arg, timeInterval, originalTime) for arg in tokens.call.args]
return func(*args)
elif tokens.number:
if tokens.number.integer:
return int(tokens.number.integer)
elif tokens.number.float:
return float(tokens.number.float)
elif tokens.string:
return str(tokens.string)[1:-1]
def evaluateTokens(tokens, timeInterval):
if tokens.expression:
return evaluateTokens(tokens.expression, timeInterval)
elif tokens.pathExpression:
pathExpr = tokens.pathExpression
if pathExpr.lower().startswith('graphite.'):
pathExpr = pathExpr[9:]
seriesList = []
(startTime,endTime) = timeInterval
for dbFile in settings.STORE.find(pathExpr):
log.metric_access(dbFile.metric_path)
getCacheResults = CarbonLink.sendRequest(dbFile.real_metric)
dbResults = dbFile.fetch( timestamp(startTime), timestamp(endTime) )
results = mergeResults(dbResults, getCacheResults())
if not results:
continue
(timeInfo,values) = results
(start,end,step) = timeInfo
series = TimeSeries(dbFile.metric_path, start, end, step, values)
series.pathExpression = pathExpr #hack to pass expressions through to render functions
seriesList.append(series)
return seriesList
elif tokens.call:
func = SeriesFunctions[tokens.call.func]
args = [evaluateTokens(arg, timeInterval) for arg in tokens.call.args]
try:
return func(*args)
except:
return []
elif tokens.number:
if tokens.number.integer:
return int(tokens.number.integer)
elif tokens.number.float:
return float(tokens.number.float)
elif tokens.string:
return str(tokens.string)[1:-1]
