def __init__(self, name, biased=True, percentiles=None):
self.name = name
if biased:
self.sample = ExpDecSample(1028, 0.015)
else:
self.sample = UniformSample(1028)
self.percentiles = self._fmt(percentiles or (75, 85, 90, 95, 99, 99.9))
self.count = 0
self.sum = 0
self.minv = None
self.maxv = None
self.variance = (-1.0, 0.0)
class Histogram(Metric):
def __init__(self, name, biased=True, percentiles=None):
self.name = name
if biased:
self.sample = ExpDecSample(1028, 0.015)
else:
self.sample = UniformSample(1028)
self.percentiles = self._fmt(percentiles or (75, 85, 90, 95, 99, 99.9))
self.count = 0
self.sum = 0
self.minv = None
self.maxv = None
self.variance = (-1.0, 0.0)
def clear(self):
self.sample.clear()
self.count = 0
self.sum = 0
self.minv = None
self.maxv = None
self.variance = (-1.0, 0.0)
def update(self, value):
self.count += 1
self.sum += value
self.sample.update(value)
if self.minv is None or value < self.minv:
self.minv = value
if self.maxv is None or value > self.maxv:
self.maxv = value
self._update_variance(value)
def metrics(self):
ret = []
ret.append(MV("%s.count" % self.name, self.count))
ret.append(MV("%s.sum" % self.name, self.sum))
ret.append(MV("%s.min" % self.name, self.minv))
ret.append(MV("%s.max" % self.name, self.maxv))
if self.count > 0:
ret.append(MV("%s.mean" % self.name, self.sum / self.count))
ret.append(MV("%s.stddev" % self.name, self._stddev()))
for disp, val in self._percentiles():
name = "%s.%s" % (self.name, disp)
ret.append(MV(name, val))
return ret
def _stddev(self):
if self.count <= 1:
return 0.0
return math.sqrt(self.variance[1] / (float(self.count) - 1.0))
def _update_variance(self, value):
oldm, olds = self.variance
if oldm == -1.0:
self.variance = (value, 0.0)
return
newm = oldm + ((value - oldm) / self.count)
news = olds + ((value - oldm) * (value - newm))
self.variance = (newm, news)
def _percentiles(self):
values = self.sample.values()
values.sort()
ret = []
for (p, d) in self.percentiles:
pos = p * len(values + 1)
if pos < 1:
ret.append((d, values[0]))
elif pos >= len(values):
ret.append((d, values[-1]))
else:
lower, upper = values[int(pos - 1)], values[int(pos)]
percentile = lower + ((pos - math.floor(pos)) * (upper - lower))
ret.append((d, percentile))
return ret
def _fmt(self, percentiles):
ret = []
for p in percentiles:
d = "%0.1f" % p
if d.endswith(".0"):
d = p[:-2]
d = "perc_%s" % d.replace(".", "_")
ret.append((p, d))
