Data Configuration for Points

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Data_Configuration_for_Points Description: [[TermDesc::This is a python dictionary filled out by a person. Contains coverage names, fields of each coverage, and the SQL table and column names for each field. The Capabilities Processor and the Coverage Processor uses this configuration information.]]

Glossary Domain: WCS

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[[Links::Capabilities Processor Coverage Processor for Points Python Dictionaries]]


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There should be a netCDF-CF like convention for SQL databases. That would allow the point coverage processor just connect to the DB and serve, without any other configuration. But since such convention does not exist, manual configuration is needed.

From file

Coverage information and it's descriptions:

   point_info = {
               'Title':'Surface Meteorological Observations',
               'Abstract':'Dummy test data.',

The covered area and time. The Time dimension is a true dimension here, but contrary to grid data, the X-Y dimensions for point data are not dimensions, but attributes of the location dimension. Time dimension format is ISO 8601 (start-inclusive)/(end-inclusive)/periodicity. PT1H means Periodicity Time 1 Hour, P1D would mean Periodicity Time 1 Day

                   'X':(-180, 179.75), 
                   'Y':(-90, 89.383),

Then comes the description of the fields.

                       'datatype': 'float',
                       'units':'deg F',

The location table is a real dimension, Latitude and Longitude are attributes along location axis, not dimensions themselves. So a typical point dataset with locations and regular time intervals is a 2-dimensional dataset. In this case, the location table is shared, so we use the previously declared variable 'location_info' If the location tables are parameter specific, they need to be specified individually.


The access instructions. This configuration is using 'complete_view', so the administrator has created the view that joins together the location table and the temperature data table. The SQL query will typically look like select loc_code, lat, lon, datetime, temp, flag from TEMP_V where datetime = '2009-09-01 and (lat between 34 and 44) and (lon between -90 and -80). This is by far the easiest way to configure the WCS.

                           'columns':['loc_code','lat','lon','datetime','temp', 'flag'],

By creating a custom handler, it is possible to store data anywhere. You still need to declare the configuration in a python dictionary, just like above.