Re: Query

Hi Eugene,

> Can anyone tell me what the error modes in the Data class mean? I can find
> three so far which is:
> DEPENDENT error estimation mode
> INDEPENDENT error estimation Mode
> NO_ERRORS error estimation Mode (this one looks easy enough)
> Is there more that I haven't found?

No, that's all of them.  These values are passed as the
error_mode argument to the Data.binary(), Data.unary()
and Function.resample() methods.  If the Data inputs to
these operations include non-null ErrorEstimate objects,
this mode specifies how they should be combined to
construct ErrorEstimates for the output Data.

NO_ERRORS says don't propogate (i.e., set ErrorEstimate
to null in the output).  INDEPENDENT says assume errors
in input Data are distributed independently, so errors grow
as root mean square (slow "drunkard's walk" growth).
DEPENDENT says assume errors in input data have dependent
distributions, so errors are additive (fast sober walk
growth, essentially interval arithmetic).

Here is the brief Section 3.6 from the Developers Guide:

3.6 ErrorEstimates
    The ErrorEstimate class contains an estimate of the variance of error
associated with a value or a set of values.  ErrorEstimates are included with
individual Real values, and with each RealType component in the range of
FlatFields.  For example, one range component of a FlatField may consist of all
temperature values in a model output grid, and these would be associated with a
single average ErrorEstimate (see Section 3.9).
    Data operations include options to propagate ErrorEstimates assuming that
errors are distributed either independently or dependently, as well as an option
to not propagate ErrorEstimates.
    The VisAD ErrorEstimates are not a substitute for a detailed error analysis,
but can provide a quick estimate of error magnitude and the possible need for
detailed analysis.

3.6.1 ErrorEstimate Constructors
. . .

Bill Hibbard, SSEC, 1225 W. Dayton St., Madison, WI  53706
hibbard@xxxxxxxxxxxxxxxxx  608-263-4427  fax: 608-263-6738

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