def geo_mean(values):
"""
GIVEN AN ARRAY OF dicts, CALC THE GEO-MEAN ON EACH ATTRIBUTE
"""
agg = Struct()
for d in values:
for k, v in d.items():
if v != 0:
agg[k] = nvl(agg[k], ZeroMoment.new_instance()) + Math.log(Math.abs(v))
return {k: Math.exp(v.stats.mean) for k, v in agg.items()}
def geo_mean(values):
"""
GIVEN AN ARRAY OF dicts, CALC THE GEO-MEAN ON EACH ATTRIBUTE
"""
agg = Dict()
for d in values:
for k, v in d.items():
if v != 0:
agg[k] = coalesce(
agg[k], ZeroMoment.new_instance()) + Math.log(Math.abs(v))
return {k: Math.exp(v.stats.mean) for k, v in agg.items()}
def stats(values):
"""
RETURN LOTS OF AGGREGATES
"""
if values == None:
return None
values = values.map(float, includeNone=False)
z = ZeroMoment.new_instance(values)
s = Struct()
for k, v in z.dict.items():
s[k] = v
for k, v in ZeroMoment2Stats(z).items():
s[k] = v
s.max = MAX(values)
s.min = MIN(values)
s.median = pyLibrary.stats.median(values, simple=False)
s.last = values.last()
s.first = values[0]
if Math.is_number(s.variance) and not Math.is_nan(s.variance):
s.std = sqrt(s.variance)
return s
def stats(values):
"""
RETURN LOTS OF AGGREGATES
"""
if values == None:
return None
values = values.map(float, includeNone=False)
z = ZeroMoment.new_instance(values)
s = Dict()
for k, v in z.dict.items():
s[k] = v
for k, v in ZeroMoment2Stats(z).items():
s[k] = v
s.max = MAX(values)
s.min = MIN(values)
s.median = pyLibrary.maths.stats.median(values, simple=False)
s.last = values.last()
s.first = values[0]
if Math.is_number(s.variance) and not Math.is_nan(s.variance):
s.std = sqrt(s.variance)
return s
def sub(self, value):
if value == None:
return
self.total -= ZeroMoment.new_instance([value])
def sub(self, value):
if value == None:
return
self.total -= ZeroMoment.new_instance([value])