This video is taken from the FDTD 100 course on Lumerical University.

## Transcript

As we saw in the previous unit, datasets are one of the common forms of data returned by

monitors.

Datasets are used to collect related data into a single object, and they contain two

parts: attributes and parameters.

Attributes are the main results of interest, and parameters contain information related

to the dimensions of the attribute.

For example if the attribute is returned at different points in time or over different

positions in space, the parameters will contain the time and position variables corresponding

to the data points.

There are three different types of datasets: matrix datasets, rectilinear spatial datasets, and

unstructured spatial datasets.

Matrix datasets contain parameters that are not related to position.

Rectilinear spatial datasets contain attributes where the data is given on a grid of x, y,

and z positions, so the parameters include x, y and z.

This is typically used to return monitor data from FDTD simulations since monitors measure

data on the rectilinear grid of the simulation mesh.

Unstructured spatial datasets contain data on a finite-element mesh like the mesh shown

here.

Monitor data in FDTD Solutions will not be returned in the form of unstructured spatial

datasets, but you might import data from a finite-element solver which will be in this

format, such as a temperature distribution profile calculated using the heat transport

solver in DEVICE.

Parameters in an unstructured spatial dataset will include x, y, and z vectors which give

the positions of each vertex on the finite-element grid, and a connectivity matrix

describing how the vertices are related.

This is a visual representation of a dataset generated from a parameter sweep where the

radius and height of a rod is varied in the sweep, and the reflection is measured.

The reflection data is a 2D matrix, and the height and radius data are 1D.

The reflection data is the attribute, and the height and radius are parameters.

Since there is no spatial position data associated, this is a matrix dataset.

This is a representation of a dataset that contains electric field data from a monitor

as a function of space and frequency.

The electric field data for each field component is a 4D matrix.

In order to represent this in the drawing, the attributes have been drawn as multiple 3D matrices

where each contains the data for one frequency point.

The x, y, z position vectors, and the frequency and wavelength are 1D

matrices.

The electric field data are the attributes and x, y, z and wavelength and frequency are

the parameters of the dataset.

Since the data is returned over a rectilinear grid of positions, the dataset is a rectilinear

spatial dataset.

One of the advantages of using datasets is that it's easy to interpret the data when

you plot it in a visualizer window since the parameters and range of parameter values are

automatically labelled.

The attributes and parameters of the datasets are listed in the visualizer window in the

tables below the plot where you can manipulate which field component to plot, and which dimensions

of the data to plot or slice.

It's also possible to package data into datasets, or extract the attribute and parameter data

from datasets using the script.

The related link below contains a tutorial showing how to package data into datasets

and extract data from datasets from the script.

On this slide is an example of a simple script where the electric field components and position

parameters are extracted from a rectilinear spatial dataset named "E" by using the dot

operator.

Now that we're familiar with the available data types, we'll look at how to access simulation

results from the graphical user interface.