Plotting in MATLAB

plotmodel

plotmodel takes the model md as first argument and then an even number of options (as in the function setelementstype, or solve). To plot a given field, use the option 'data' followed by the field one wants to plot. For the thickness:

>> plotmodel(md, 'data', md.geometry.thickness)

You can plot several fields at the same time but you have to add the argument 'data' before each field you want to plot:

>> plotmodel(md, 'data', md.geometry.thickness, 'data', 'mesh', 'data', [1:md.mesh.numberofelements])
Figure 1: plot

This can work for any field of length md.mesh.numberofelements or md.mesh.numberofvertices.

Options

Options in plotmodel come as pairs: the option name must be followed by its value. For example, if one wants to remove the color bar, the option name is 'colorbar' and the value 0:

>> plotmodel(md, 'data', md.initialization.vel, 'colorbar', 0)

any options (except 'data') can be followed by '#<i>' where <i> is the subplot number, or '#all' if applied to all plots. For example:

>> plotmodel(md, 'data', md.initialization.vel, 'data', 'mesh', 'view#2', 3, 'colorbar#all', 'on', 'axis#1', 'off equal')

axis

Same as standard axis MATLAB option:

>> plotmodel(md, 'data', md.vel, 'axis', 'tight')

view

Same as standard view MATLAB option:

>> plotmodel(md, 'data', md.vel, 'view', 2)

xlim, ylim, zlim

Same as standard xlim MATLAB option:

>> plotmodel(md, 'data', md.vel, 'xlim', [10^5 2*10^5])

caxis

Same as standard caxis MATLAB option (control the extreme values of the colorbar):

>> plotmodel(md, 'data', md.vel, 'caxis', [0 1000])
Figure 2: caxis

colorbar

This option is used to control the colorbar display ('on' or 'off'):

>> plotmodel(md, 'data', md.vel, 'colorbar', 'off')

colormap

Same as standard colormap MATLAB option (control the extreme values of the colorbar):

>> plotmodel(md, 'data', md.vel, 'colormap', 'hsv')
Figure 3: colormap

log

To get a logarithmic colorbar, use the 'log' option followed by 10 for a decimal logarithm:

>> plotmodel(md, 'data', md.vel, 'log', 10)
Figure 4: log

contourlevels

Contours of equi-value can be added to the plot by using the 'contourlevels' option. The number of contours can be chosen by using the 'contourlevels' options. The user can specify a number of levels or a cell containing the values of color changes. For example:

>> plotmodel(md, 'data', md.vel, 'contourlevels', 3)
Figure 5: contour3
>> plotmodel(md, 'data', md.vel, 'contourlevels', {100, 200, 500, 1000, 2000, 2500})
Figure 6: contourcell

contourticks

If the user does not want to display the contour levels ticks, use the 'contourticks' set as 'off':

>> plotmodel(md, 'data', md.vel, 'contourlevels', {100, 200, 500, 1000, 2000, 2500}, 'contourticks', 'off')
Figure 7: contourticks

contouronly

If the user wants to display the contours only, use the 'contouronly' set as 'on':

>> plotmodel(md, 'data', 'vel', 'contourlevels', {100, 200, 500, 1000, 2000, 2500}, 'contouronly', 'on')

streamlines

Streamlines can be displayed by using the 'streamlines' option followed by a number of streamlines or a cell containing the coordinates of seed points:

>> plotmodel(md, 'data', md.initialization.vel, 'streamlines', 50)

>> plotmodel(md, 'data', md.initialization.vel, 'streamlines', {10^6 * [-1.45 -0.27], 10^6 * [-1.6 0]})
Figure 8: streamlines50Figure 8: streamlinescell

NOTE: Streamlines use the velocities that are in md.initialization. Make sure you transfer the calculated velocities to md.initialization if you want to display the calculated streamlines.

edgecolor

The mesh can be superimposed onto the plot by using the 'edgecolor' option followed by a color:

>> plotmodel(md, 'data', md.initialization.vel, 'edgecolor', 'w')
Figure 9: edgecolor

expdisp

Any ARGUS file can be displayed with the 'expdisp' option followed by the name of the ARGUS file:

>> plotmodel(md, 'data', md.initialization.vel, 'expdisp', 'Iceshelves.exp')

expstyle

The style of the ARGUS profile can be controlled with the 'expstyle' option, followed by the desired line style. Here is an example for a yellow dotted line:

>> plotmodel(md, 'data', md.initialization.vel, 'expdisp', 'Iceshelves.exp', 'expstyle', '--y')

mask

If one does not want to display the value of the field on a mask only, use the 'mask' option followed by a vector that holds 0 for the vertices whose values are hidden:

>> plotmodel(md, 'data', md.initialization.vel, 'mask', md.mask.ocean_levelset < 0)
Figure 10: mask

northarrow

An arrow pointing North can be added with the 'northarrow' option followed by 'on'. The shape and position of the arrow can be controlled by using [x0 y0 length [ratio [width]]] instead of 'on':

>> plotmodel(md, 'data', md.initialization.vel, 'northarrow', 'on')

scaleruler

A scale ruler can be added. As for the North arrow, the default display is done by 'on' but the shape and position of the scale ruler can be controlled by [x0 y0 length width numberofticks] where (x0,y0) are the coordinates of the lower left corner:

>> plotmodel(md, 'data', md.initialization.vel, 'scaleruler', 'on')

title

Same as standard title MATLAB option:

>> plotmodel(md, 'data', md.vel, 'title', 'Ice velocity [m/yr]')

fontsize

Same as standard fontsize MATLAB option:

>> plotmodel(md, 'data', md.vel, 'title', 'Ice velocity [m/yr]', 'fontsize', 8)

fontweight

Same as standard fontweight MATLAB option:

>> plotmodel(md, 'data', md.vel, 'title', 'Ice velocity [m/yr]', 'fontweight', 'b')

xlabel, ylabel

Same as standard xlabel MATLAB option:

>> plotmodel(md, 'data', md.vel, 'xlabel', 'x axis [m]')

Special plots

basaldrag

The special plot 'basal_drag' displays the norm of the basal drag friction in kPa following formula:

Equation 1

Basal drag relies on the velocity provided in md.initialization. The x and y components of the basal drag can be displayed with the 'basal_dragx' or 'basal_dragy' special plots:

>> plotmodel(md, 'data', 'basal_drag')

>> plotmodel(md, 'data', 'basal_dragx')
Figure 11: basaldragFigure 11: basaldragcomp

Basal friction norm and Basal friction x-component

BC

The special plot 'BC' displays all boundary conditions (Newmann and Dirichlet) for 2D and 3D meshes:

>> plotmodel(md, 'data', 'BC')
Figure 12: BC

driving_stress

The special plot 'driving_stress' displays the basal drag friction in kPa following formula:

Equation 2
>> plotmodel(md, 'data', 'driving_stress')
Figure 13: driving_stress

elementnumbering

In the debugging process, it is often very useful to display all the elements next to their numbers. This is what the special plot 'elementnumbering' does:

>> plotmodel(md, 'data', 'elementnumbering')
Figure 14: elementnumbering

A given list of elements can be highlighted with the 'highlight' option:

>> plotmodel(md, 'data', 'elementnumbering', 'highlight', [3 4 5 6 7])
Figure 15: elementnumbering_highlight

elements_type

The special plot 'elements_type' displays the elements with a specific color for each formulation:

>> plotmodel(md, 'data', 'elements_type')
Figure 16: elements_type

vertexnumbering

In the debugging process, it is often very useful to display all the vertices next to their numbers. This is what the special plot 'vertexnumbering' does:

>> plotmodel(md, 'data', 'vertexnumbering')
Figure 17: gridnumbering

A given list of vertices can be highlighted with the 'highlight' option:

>> plotmodel(md, 'data', 'vertexnumbering', 'highlight', [2 5 7 12])
Figure 18: gridnumbering_highlight

highlightelements

The special plot 'highlightelements' is very similar to the plot 'elementnumbering'. It is another possibility to highlight one or several grids, but without indicating the number of all the elements. It is a lot faster for large models:

>> plotmodel(md, 'data', 'highlightelements', 'highlight', 5)

>> plotmodel(md, 'data', 'highlightelements', 'highlight', [5 12])
Figure 19: highlightelements_highlightFigure 19: highlightelements_highlight3d

highlightgrids

The special plot 'highlightgrids' is very similar to 'gridnumbering'. It is another possibility to highlight grids without indicating all the grids numbers. It is a lot faster for big models:

>> plotmodel(md, 'data', 'highlightgrids', 'highlight', [12 20])

>> plotmodel(md, 'data', 'highlightgrids', 'highlight', [12 16 26])
Figure 20: highlightgrids_highlightFigure 20: highlightgrids_highlight3d

icefront

The special plot 'icefront' displays the Neumann boundary conditions, i.e. all the segments on ice front and the normal to these segments, for a 2D or 3D mesh:

>> plotmodel(md, 'data', 'icefront')
Figure 21: icefront2dFigure 21: icefront3d

mesh

The special plot 'mesh' displays the mesh of 2D or 3D model:

>> plotmodel(md, 'data', 'mesh')
Figure 22: mesh2dFigure 22: mesh3d

Quiver plot

For 2D or 3D fields, a generic color plot cannot be used (except component by component). The 'data' used by the function plotmodel must be a matrix of 2 or 3 columns. For example:

>> plotmodel(md, 'data', [md.vx md.vy])
Figure 23: plotquiver

ColorLevels

The number of colors can be chosen by using the 'colorlevels' options. The user can specify a number of levels or a cell containing the values of color changes. For example:

>> plotmodel(md, 'data', [md.vx md.vy], 'colorlevels', 3)

>> plotmodel(md, 'data', [md.vx md.vy], 'colorlevels', 100)
Figure 24: color3Figure 24: color100
>> plotmodel(md, 'data', [md.vx md.vy], 'colorlevels', {100, 200, 500, 1000, 2000, 2500})
Figure 25: colorcell

Scaling

The arrows length can be modified with the 'scaling' options. The default value is 0.4. A higher scaling value will result in longer arrows:

>> plotmodel(md, 'data', [md.vx md.vy], 'scaling', 1)

>> plotmodel(md, 'data', [md.vx md.vy], 'scaling', 0.1)
Figure 26: scaling1Figure 26: scaling01

Autoscale

If the user wants all the arrows to have the same length, use the option 'autoscale' set as 'off':

>> plotmodel(md, 'data', [md.vx md.vy], 'autoscale', 'off')
Figure 27: autoscale

Density

The number of arrows can be reduced with the option 'density'. If the density is set as 3, only one arrow out of 3 will be displayed. This option is very useful when the mesh is very refined:

>> plotmodel(md, 'data', [md.vx md.vy], 'density', 3)
Figure 28: density3

Cross section

The section plot can be used to display the value of a field on a given track. The option 'sectionvalue' must be followed by the name of an ARGUS file which contained the coordinates of the points describing the profile (this file can be generated by exptool.m). The resulting plot will be a curve in 2D and a colored surface in 3D. For example:

>> plotmodel(md, 'data', md.vel, 'expdisp', 'track.exp')
Figure 29: track
>> plotmodel(md, 'data', md.vel, 'sectionvalue', 'track.exp')
Figure 30: 2dFigure 30: 3d

Section plot for 2D (left) and 3D (right) models

Resolution

The horizontal and vertical (in 3D) resolution can be specified by the 'resolution' option. It must be a list with the horizontal resolution followed by the vertical resolution (in meters). When not specified, the default resolution is displayed:

>> plotmodel(md, 'data', md.vel, 'sectionvalue', 'track.exp', 'resolution', [2*10^4 0])

>> plotmodel(md, 'data', md.vel, 'sectionvalue', 'track.exp', 'resolution', [10^3 0])

Show section

The profile used to create the section plot can be also plotted with the 'showsection' option:

>> plotmodel(md, 'data', md.vel, 'showsection', 'on')
Figure 31: show