laminate [ parameter=value ... ] [ inputfile ... outputfile ]
Parameters are: include_vars, laminate_attrs, new_dim_name, keep_hist.
laminate takes common variables from multiple input datasets and laminates them along a newly specified dimension. Any or all variables in the datasets can be laminated. In addition to lamination of variables, attributes can also be laminated, thus becoming variables parallel to the new dimension. The laminated data is written to a single output dataset. The new dimension name is specified by the user.
Each of the input datasets is assumed to have the same variable names, data types, bad values, units, and scaling as the first input dataset. An error is returned if the variable names, size of the dimensions, or data type do not all match the first input dataset.
The earth transform from the first dataset is copied to the output. If an earth transform does not exist for the first dataset, the output dataset will not have an earth transform.
Specifies which variables in the input datasets are to be laminated and written to the output dataset. If the list is preceded by a minus sign, all variables, except those listed, will be laminated. Wildcards * and ? are allowed.
The default is all variables in the input datasets.
Optionally specifies a list of attributes to laminate and write to the output dataset.
The default implies none.
Specifies the name of the new dimension along which the data is to be laminated.
There is no default.
OPTIONAL. If keep_hist=yes, then the command and input file histories are appended to the output file history (see audit and hist). This is the usual case with most TeraScan functions. However, since the number of input files in this case is presumed large, this would result in a very large history attribute, and, by default, the history is not appended for this function.
Valid responses are [yes, no]. The default value is no. This parameter can only be set by an explicit specification on the command line.
As an example, let's assume we have variables v1, v2, and v3 in datasets X1...XN, where X1...XN are in temporal order and each contains a attribute specifying its time of observation. Furthermore, for each variable vN, N corresponds to the number of dimensions. Thus v1 is 1-D, v2 is 2-D, and v3 is a 3-D variable. The following example laminates the data in the X datasets, creating a new dataset Y.
temp_19_% laminate in/out files : char(255) ? X* Y include_vars : char(255) ? [] laminate_attrs : char(255) ? [] pass_date new_dim_name : char( 31) ? time
The new dataset Y has variables v1, v2, v3, and pass_date. The variable pass_date is a 1-D variable with dimension time. The vN variables now have n+1 dimensions, with time being the new dimension.
burst2, datasets, samplam, sample, dimavg, emath, composite.
Last Update: $Date: 1998/05/29 18:32:58 $