def __init__(self, period, metric=None):
ProxiedMetric.__init__(self, metric)
self.period = period
self.lastData = None
self.percentChange = PercentChange(metric)
self._addMetric(self.percentChange)
self.changes = list()
self.val = None
def __init__(self, period, metric=None):
ProxiedMetric.__init__(self, metric)
self.period = period
self.lastData = None
self.percentChange = PercentChange(metric)
self._addMetric(self.percentChange)
self.changes = list()
self.val = None
class PercentRank(ProxiedMetric):
def __init__(self, period, metric=None):
ProxiedMetric.__init__(self, metric)
self.period = period
self.lastData = None
self.percentChange = PercentChange(metric)
self._addMetric(self.percentChange)
self.changes = list()
self.val = None
def ready(self):
if self.val == None:
return False
else:
return True
def value(self):
return self.val
def handle(self, perioddata):
ProxiedMetric.handle(self, perioddata)
if self.percentChange.ready():
nextchg = self.percentChange.value()
if len(self.changes) == self.period:
# have enough to calculate value, do it now before storing our latest value
count = 0.0
for pc in self.changes:
if pc < nextchg:
count = count + 1.0
self.val = 100 * (count / float(self.period))
self.changes.append(nextchg)
if len(self.changes) > self.period:
self.changes = self.changes[len(self.changes) - self.period:]
pass
def recommendedPreload(self):
return ProxiedMetric.recommendedPreload(self) + self.period
class PercentRank(ProxiedMetric):
def __init__(self, period, metric=None):
ProxiedMetric.__init__(self, metric)
self.period = period
self.lastData = None
self.percentChange = PercentChange(metric)
self._addMetric(self.percentChange)
self.changes = list()
self.val = None
def ready(self):
if self.val == None:
return False
else:
return True
def value(self):
return self.val
def handle(self, perioddata):
ProxiedMetric.handle(self, perioddata)
if self.percentChange.ready():
nextchg = self.percentChange.value()
if len (self.changes) == self.period:
# have enough to calculate value, do it now before storing our latest value
count = 0.0
for pc in self.changes:
if pc < nextchg:
count = count+1.0
self.val = 100*(count/float(self.period))
self.changes.append(nextchg)
if len(self.changes) > self.period:
self.changes = self.changes[len(self.changes)-self.period:]
pass
def recommendedPreload(self):
return ProxiedMetric.recommendedPreload(self) + self.period
