3D Graphics Methods

Deriving actionable information from climate simulations requires the capacity to detect, compare, and analyze features spanning large heterogeneous, multi-variate, multi-dimensional datasets with spatial and temporal references. The brain’s capacity to detect visual patterns is invaluable in this knowledge discovery process. Visual mapping techniques are very effective in expressing the results of feature detection and analysis algorithms as they naturally employ the visual information processing capacity of the cerebral cortex, which is extremely difficult to emulate using statistical and machine learning approaches alone. Visual representations, which play an important role in addressing data complexity, can be enhanced by an increase in the number of “degrees of freedom” in the visual mapping process. Interactive three-dimensional views into complex high dimensional datasets can offer a widened perspective and a more comprehensive gestalt facilitating the recognition of significant features and the discovery of important patterns and relationships in the climate knowledge discovery process.

3D Plot Constituents

In the VCS model 3D perspectives are provided by the 3d_scalar and 3d_vector graphics methods.

The 3d_scalar graphics method provides the Volume, Surface, and Slice display techniques (denoted henceforth as “plot constituents”). It can be used to display data in both the default (x-y-z) and Hovmoller3D (x-y-t)geometries.

The 3d_vector graphics method provides the Vector slice plot constituent.

Volume

The Volume plot enables scientists to create an overview of the topology of the data, revealing complex 3D structures at a glance. It is generated using a “transfer function” to linearly map an adjustable range of variable values to an adjustable range of opacity values at each point of a 3D volume. Values of the variable that fall outside of the range are invisible (transparent). In addition, the rendered color is determined by mapping the variable’s value at each point of the volume to an adjustable range of colormap values. All three adjustable ranges can be configured either statically using a script or interactively using sliders in an active plot window.

Surface

The Surface plot can produce views similar to a volume rendering while facilitating the comparison of two variables. It is displayed as an isosurface (the higher dimensional analog of an isoline or contour line on a weather or terrain map), illustrating the surfaces of constant value for one variable and optionally colored by the spatially correspondent values of a second variable. The rendered color is determined by mapping the second variable’s value at each point of the surface to an adjustable range of colormap values. The isosurface value and the colormap range can be configured either statically using a script or interactively using sliders in an active plot window.

Slice

The Slice plot allows scientists to quickly and easily browse the 3D structure of a dataset, compare variables in 3D, and probe data values. It provides a set of three slice planes (perpendicular to the x, y, and z axes) that can be interactively dragged over the dataset. A slice through the data volume at the plane’s location is displayed by mapping the variable’s value at each point of the plane to an adjustable range of colormap values. A slice through a second data volume can also be overlaid as a contour map over the first. In an active plot window a shift-right-click on one of the planes will display the coordinates and value of the variable(s) at that point. The slice positions and the colormap range can be configured either statically using a script or interactively using sliders in an active plot window

Vector

The Vector slice plot allows scientists to browse the 3D structure of variables (such as wind velocity) that have both magnitude and direction. It provides a horizontal slice plane that can be interactively dragged over a vector field dataset (consisting of a pair of variables denoting the X and Y components of a vector at each point). A slice through the data volume at the plane’s location is displayed using a set of vector glyphs denoting the direction and magnitude of the field at a regularly spaced set of points on the plane. The slice position and the density and scaling of the vector glyphs can be configured either statically using a script or interactively using sliders in an active plot window.

Note

This display technique can be very computationally intensive so that the higher glyph densities may cause diminished interactivity.

If in interact mode (see vcs.Canvas.Canvas.interact()), these attributes can be configured interactively, via the method described in the Interact Mode section of the attribute description.

class vcs.dv3d.Gf3DDualScalar(Gfdv3d_name, Gfdv3d_name_src='default')[source]
class vcs.dv3d.Gf3Dscalar(Gfdv3d_name, Gfdv3d_name_src='default')[source]
class vcs.dv3d.Gf3Dvector(Gfdv3d_name, Gfdv3d_name_src='default')[source]
class vcs.dv3d.Gfdv3d(Gfdv3d_name, Gfdv3d_name_src='default')[source]

Gfdv3d is class from which Gf3Dvector, Gf3Dscalar, and Gf3DDualScalar inherit. It sets up properties and functions common to all of the 3d graphics method objects.

.. py

Toggles the visibility of the dv3d-Surface (isosurface) plot constituent.

Interact Mode: Toggle visibility with button click.

Type

attribute:: ToggleSurfacePlot (vcs.on or vcs.off)

ToggleVolumePlot(vcs.on or vcs.off)

Toggles the visibility of the dv3d-Volume (volume render) plot constituent.

Interact Mode: Toggle visibility with button click.

XSlider(float, vcs.on or vcs.off)

Sets the position and visibility of the X Slice plane. The position is in longitude coordinates.

Interact Mode: Adjust position with the slider.

YSlider(float, vcs.on or vcs.off)

Sets the position and visibility of the Y Slice plane. The position is in latitude coordinates.

Interact Mode: Adjust position with the slider.

ZSlider(float, vcs.on or vcs.off)

Sets the position and visibility of the Z Slice plane. The position is in relative (0.0 = bottom, > 1.0 = top) coordinates.

Interact Mode: Adjust position with the slider.

VerticalScaling(float)

Scales the vertical dimension of the plot.

Accepts values from ~ 0.1 – 10.0

Interact Mode: Adjust position with the slider.

ScaleColormap(floats: [max,min])

Sets the value range of the current colormap. Initialized to the max (full) range value of the data.

Interact Mode: Adjust colormap range (min, max) with the pair of sliders.

ScaleTransferFunction(floats: [max,min])

Sets the value range of the Volume plot constituent, which maps this range of variable values to opacity. Initialized to the max (full) range value of the data.

Interact Mode: Adjust TF range (min, max) with the pair of sliders.

ToggleClipping(Up to six floats: [ xmin, xmax, ymin, ymax, zmin, zmax ])

Sets the clip bounds for the Volume plot constituent.

Interact Mode: Drag the spheres on the adjustable frame.

IsosurfaceValue(float between the variable max and min values)

Sets the variable value that defines the isosurface (Surface).

Interact Mode: Adjust the isosurface value using the slider.

ScaleOpacity(floats: [ max, min ])

Sets the opacity range of the Volume plot constituent, which maps the selected range of variable values to this opacity range. Initialized to [1,1]

Interact Mode: Adjust opacity range (min, max) with the pair of sliders.

BasemapOpacity(float between 0.0 and 1.0.)

Sets the opacity of the underlying earth map.

Interact Mode: Adjust the opacity with the slider.

Camera(dict with three keys: 'Position', 'ViewUp', and 'FocalPoint')

Sets the position and orientation of the camera.

The values of Position and FocalPoint are positions in model coordinates, and ViewUp is a unit vector.

Interact Mode: Left-click in window and drag to rotate. Right-click and drag to zoom/pan. Shift-Left-click and drag to translate.

Jupyter Notebooks