Hi,
On 03/04/18 14:51, marceloandrioni@xxxxxxxxxxxxxxxx wrote:
Hello, I have a FMRC aggregation of several yearly files with the usual
variables (ssh,t,s,u,v), e.g.
<featureCollection name="mercator" featureType="FMRC" harvest="true"
path="mercator">
<collection spec="/home/opendap/datasets/mercator/**/mercator_#yyyy#\.nc"/>
<update startup="true"/>
<netcdf xmlns="http://www.unidata.ucar.edu/namespaces/netcdf/ncml-2.2";>
</netcdf>
<fmrcConfig regularize="false" datasetTypes="Best Files" />
</featureCollection>
I have a different file with non time dependent variables (bathymetry, sea land mask, etc) and
would like to have these variables also appear in the FMRC. I tried just renaming the file to
mercator_1970.nc hoping that the <collection
spec="/home/opendap/datasets/mercator/**/mercator_#yyyy#\.nc"/> would catch it,
but it didn't work. My guess is that doesn't work because there is no time dimension in this
file and time is necessary for FMRC aggregation.
No. It's because the prototype dataset been chosen by the
featureCollection doesn't contain those variables. By default the
featureCollection choose the penultimate dataset/file to build-up the
resulting dataset with all dataset/files in the collection.
Besides appending the non time dependent variables to every single file with
ncks -A bathymetry_mask.nc mercator_YYYY.nc
is there a way using ncml to join all the files (with and without time
dimensions) in the FMRC?
You can change the default prototype dataset by:
<featureCollection name="mercator" featureType="FMRC" harvest="true"
path="mercator">
<collection spec="/home/opendap/datasets/mercator/**/mercator_#yyyy#\.nc"/>
<update startup="true"/>
*<protoDataset **choice**="First"/>*
<fmrcConfig regularize="false" datasetTypes="Best Files" />
</featureCollection>
and append the missing variables to the first dataset/file in the collection,
i.e.
ncks -A bathymetry_mask.nc mercator_1970.nc
Then they should be "appear" in the resulting dataset. From the doc [1] you can
read
/.... The choice of the protoDataset matters when the datasets are not
homogenous://////Global and variable attributes are taken from the prototype dataset.////If a variable appears in the prototype dataset, it will appear in the
feature collection dataset. If it doesnt appear in other datasets, it
will have missing data for those times.////If a variable does not appears in the prototype dataset, it will not
appear in the feature collection dataset, even if it appears in other
datasets./
....
Also, I am adamant in using FMRC because my tests showed that is a lot faster
to retrieve data than using aggregation joinNew or joinExisting.
In my case I'm reluctant to use FMRC because you have less control on
building the resulting dataset. With pure ncml and aggregations you have
better control on resulting dataset and good performance if you control
cache and definition of the coordinate values.
Regards
Antonio
[1]
https://www.unidata.ucar.edu/software/thredds/v4.6/tds/reference/collections/FeatureCollections.html#elements
--
Antonio S. Cofiño
Dep. de Matemática Aplicada y
Ciencias de la Computación
Universidad de Cantabria
http://www.meteo.unican.es
