[QUANTITY] Plea for pragmatism

[David Berry]

Doug,

> Here is a first cut at the component data models needed for data
> characterization.  This is based on further development of our Cambridge
> etc. discussions.
>
> Coverage / Characterization Component Data Models
>
>     sky coverage
>         coverage on the sky, if applicable
>         e.g., a circular or rectangular region or aperture on the sky
> 	Can be used to estimate the WCS but the full WCS should be
> 	defined elsewhere.
>
>     time coverage / bandpass
>         time of observation
>         refValue, hiValue, loValue, fillFactor
>         refValue is the mean time of observation, e.g., mid-point
>
>     spectral bandpass
>         range of spectral frequencies in data
>         id, refValue, hiValue, loValue, units, fillFactor
>         id is user-defined bandpass name, e.g., "V", "SDSS_U", "K-Band", etc.
>         refValue is the characteristic frequency of the bandpass
>
>     spatial bandpass
>         range of spatial frequencies in data
>         hiValue, loValue, units, fillFactor (no refValue)
>         loValue is also known as the spatial resolution
>
>     flux bandpass
>         range of flux values in data
>         hiValue, loValue, units, fillFactor (no refValue)
>         loValue is also known as the limiting flux or magnitude
>         hiValue is saturation limit or maximum flux
>
...
> Did I leave anything out?  Polarization perhaps, but that might be better
> dealt with elsewhere.

Where does spectral resolution come in this?

Is the idea that a "coverage" component gives a summary of the ranges
taken by significant parameters of the observation? For instance,
spatial coverage is just a summary of the WCS (i.e. you could work out
the spatial coverage by using the WCS to check every pixel). In this
case, we need to make clear what happens to coverage when the observation
is modified (e.g. taking a cut out will modify the spatial coverage,
filters may change the effective spectral bandpass of an image, etc).
So does coverage describe the data set "as it is now" (i.e. post
processing) or as it was "in the beginning" (i.e. pre processing) -
presumably the former.

Also, in the same way that the spatial coverage is just a summary of the
WCS, presumably the same could be true of other aspects of coverage. For
instance, the "spectral bandpass" coverage  may be a summary of some more
sophisticated spectral bandpass component. Thus it would be inappropriate
to put a sophisticated spectral bandpass model within the coverage
component.

In general. I'm a bit unclear about the difference between the term
"bandpass" and "coverage" in your list. "Spatial coverage" and "spectral
bandpass" both seem to be similar in that they both describe the
sensitivity of the observation over their particular domain; "spatial
coverage" gives the sensitivity of the observation as a function of
spatial position, and "spectral bandpass" gives the sensitivity of the
observation as a function of spectral position. On the other hand,
"spatial bandpass" seems to be more like a resolution.

An alternative way to generalise the "coverage" problem, may be as
follows... A "coverage" has an (optional) component for each domain of
interest; spatial position, spectral position, time position, data value
and spatial frequency. Each of these component has the following
sub-components:

   sensitivity: The sensitivity of the observation as a function of
      the domain (spatial position, spectral position, etc).

   resolution:
      Typical increment between independant values in the domain

   sample size:
      Typical increment between between adjacent values in the domain

   error
      Typical uncertainties associated with values in the domain.

Not all of these sub-components may be applicable to all domains - for
instance I'm not sure what the resolution of the data value domain is
("error" would describe the data value uncertainty and "sample size"
would describe any quantisation).



David