openzgy.impl.stats.StatisticData Class Reference

Public Member Functions
def	__init__ (self, other=None)
def	__repr__ (self)
def	__str__ (self)
def	add (self, data, factor)
def	scale (self, slope, intercept)
def	__eq__ (self, other)
def	__ne__ (self, other)
def	__add__ (self, other)
def	__iadd__ (self, other)
def	__mul__ (self, factor)
def	__rmul__ (self, factor)
def	__imul__ (self, factor)

Detailed Description

Accumulate statistics: count, sum, sum of squares, and value range.

Member Function Documentation

scale()

def openzgy.impl.stats.StatisticData.scale	(		self,
			slope,
			intercept
	)

Calculate the linear transform needed to convert from one range
(typically the natural data range of the integral storage type)
to the data range that the application wants to see.
Then update the statistics in place so they look like the transform
had been done on every single data point before adding it.

The decoded value Y is given by a linear transform of the coded value X:

  Y = intercept + slope*X

where intercept and slope are given by the coding range and the value range
of type T (see below). The statistics of Y are then:

  SUM_Y = SUM(intercept + slope*x)
= n*intercept + slope*SUM(x) = n*intercept + slope*SUM_X

  SSQ_Y = SUM((intercept + slope*x)^2)
= SUM(intercept^2 + 2*intercept*slope*x + slope^2*x^2)
= n*intercept^2 + 2*intercept*slope*SUM(x) + slope^2*SUM(x^2)
= n*intercept^2 + 2*intercept*slope*SUM_X + slope^2*SSQ_X

  MIN_Y = MIN(intercept + slope*x)
= intercept + slope*MIN(x)
= intercept + slope*MIN_X

  MAX_Y = MAX(intercept + slope*x)
= intercept + slope*MAX(x)
= intercept + slope*MAX_X

---

The documentation for this class was generated from the following file:

• openzgy/impl/stats.py