Creates an empty matrix dataset. Matrix datasets are used for data (attributes and parameters) that don't have any spatial dependence (i.e. Reflection vs frequency). For datasets that do have x/y/z spatial coordinates (i.e. electric fields), use rectilineardataset or unstructureddataset.
Matrix datasets can be parameterized, and can contain an arbitrary number of attributes (see addattribute) and parameters (see addparameter).
See Dataset introduction for more information.
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Syntax |
Description |
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matrixdataset; |
Creates an empty dataset. |
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matrixdataset("name"); |
Creates an empty dataset with the name "name". |
Examples
This example uses a matrix dataset to store cross section (sigma) data as a function of frequency. In this case, the cross section data sigma is the attribute, and frequency is the parameter. To allow the user to access the frequency parameter in terms of frequency or wavelength , both frequency (f) and wavelength (c/f) are added as interdependent parameters.
sigma = matrixdataset("cross_section");
sigma.addparameter("lambda",c/f,"f",f); # add parameter f and lambda
sigma.addattribute("sigma",CS); # add attribute CS
visualize(sigma); # visualize this dataset in the Visualizer
The following script code generates some example data, then creates a R(radius,height) dataset.
# create example results
radius = 0:10;
height = 1:0.1:3;
reflection = randmatrix(length(radius),length(height));
# create Reflection dataset
R = matrixdataset("R"); # initialize dataset
R.addparameter("radius",radius); # add radius parameter
R.addparameter("height",height); # add height parameter
R.addattribute("R",reflection); # add reflection attribute
# plot data
image(radius,height,reflection); # use original matrices
image(R.radius,R.height,R.R); # use dataset
# send dataset to visualizer
visualize(R);
See Also
rectilineardataset, addattribute, addparameter, visualize, datasets, getparameter, getattribute, matrixdataset, struct