Databases
StackOverflow2013
Postsdbo
PO
 

Note that there are some explanatory texts on larger screens.

plurals
  1. PO
    primarykey
    data
    text
    <p>One option works through triangulation of all the elements and then plotting using the matplotlib <a href="http://matplotlib.org/api/axes_api.html?highlight=tri#matplotlib.axes.Axes.tripcolor" rel="nofollow noreferrer"><code>tripcolor()</code></a> function I have now discovered. Two useful demos are <a href="http://matplotlib.org/examples/pylab_examples/tripcolor_demo.html" rel="nofollow noreferrer">here</a> and <a href="http://matplotlib.org/examples/pylab_examples/triplot_demo.html?highlight=triangulation" rel="nofollow noreferrer">here</a>.</p> <p>Auto-triangulation of my global domain can be problematic, but Delaunay triangulation of a single quadrilateral works very well: <img src="https://i.stack.imgur.com/hWDMI.png" alt="triangulation displayed for just the center element"></p> <p>I create a global triangulation by appending the triangulation of each element. This means shared nodes are actually duplicated in the position arrays and value arrays. This allows for the discontinuous data at element faces. <img src="https://i.stack.imgur.com/9cebS.png" alt="triangulation displayed for all elements"></p> <p>Drawing with linear interpolation and discontinuities as desired can be achieved with the <code>tripcolor()</code> function, supplying the node locations, and values for each node. <img src="https://i.stack.imgur.com/6dVFS.png" alt="final solution pcolor"></p> <p>I was a little concerned how contour plotting might work, since element faces are no longer logically connected. <code>tricontour()</code> still works as expected. (shown here with triangulation overlaid) <img src="https://i.stack.imgur.com/IO7PU.png" alt="contour plot with triangulation overlaid"></p> <p>Reproduced with the following code:</p> <pre><code>import numpy as np import matplotlib.pyplot as plt import matplotlib.tri as tri x = np.array( [ [ [0.0, 1.0], [0.0, 1.0], [0.0, 1.0] ], #element 0 [ [1.0, 2.0], [1.0, 2.0], [1.0, 2.0] ], #element 1 [ [2.0, 3.0], [2.0, 3.0], [2.0, 3.0] ], #element 2 ] ) y = np.array( [ [ [0.0, 0.0], [0.5, 0.5], [1.0, 1.0] ], #element 0 [ [0.0, 1.0], [0.5, 1.5], [1.0, 2.0] ], #element 1 [ [1.0, 1.0], [1.5, 1.5], [2.0, 2.0] ], #element 2 ] ) z = np.array( [ [ [0.0, 0.5], [0.0, 0.8], [0.0, 1.0] ], #element 0 [ [0.3, 1.0], [0.6, 1.2], [0.8, 1.3] ], #element 1 [ [1.2, 1.5], [1.3, 1.4], [1.5, 1.7] ], #element 2 ] ) global_num_pts = z.size global_x = np.zeros( global_num_pts ) global_y = np.zeros( global_num_pts ) global_z = np.zeros( global_num_pts ) global_triang_list = list() offset = 0; num_triangles = 0; #process triangulation element-by-element for k in range(z.shape[0]): points_x = x[k,...].flatten() points_y = y[k,...].flatten() z_element = z[k,...].flatten() num_points_this_element = points_x.size #auto-generate Delauny triangulation for the element, which should be flawless due to quadrilateral element shape triang = tri.Triangulation(points_x, points_y) global_triang_list.append( triang.triangles + offset ) #offseting triangle indices by start index of this element #store results for this element in global triangulation arrays global_x[offset:(offset+num_points_this_element)] = points_x global_y[offset:(offset+num_points_this_element)] = points_y global_z[offset:(offset+num_points_this_element)] = z_element num_triangles += triang.triangles.shape[0] offset += num_points_this_element #go back and turn all of the triangle indices into one global triangle array offset = 0 global_triang = np.zeros( (num_triangles, 3) ) for t in global_triang_list: global_triang[ offset:(offset+t.shape[0] )] = t offset += t.shape[0] plt.figure() plt.gca().set_aspect('equal') plt.tripcolor(global_x, global_y, global_triang, global_z, shading='gouraud' ) #plt.tricontour(global_x, global_y, global_triang, global_z ) #plt.triplot(global_x, global_y, global_triang, 'go-') #plot just the triangle mesh plt.xlim((-0.25, 3.25)) plt.ylim((-0.25, 2.25)) plt.show() </code></pre>
    singulars
    1. This table or related slice is empty.
    1. POpython: How to plot 2D discontinuous node-centered data?
      singulars
      1. PTQuestion
    1. PTAnswer
    1. USNoahR
    1. USNoahR
    plurals
    1. This table or related slice is empty.
    1. This table or related slice is empty.
    1. POpython: How to plot 2D discontinuous node-centered data?
      singulars
      1. PTQuestion
    1. This table or related slice is empty.
    1. VO
      singulars
      1. PO
      1. This table or related slice is empty.
      1. VTUpMod
    2. VO
      singulars
      1. PO
      1. This table or related slice is empty.
      1. VTAcceptedByOriginator
    1. COremember to accept your own answer if you are happy with it.
      singulars
      1. PO
      1. UStacaswell
    2. COI have to wait two days before that is allowed, apparently.
      singulars
      1. PO
      1. USNoahR
    3. COno worries, just pinging as a reminder. In two days it will be far enough down the list I might not see it to remind you ;)
      singulars
      1. PO
      1. UStacaswell
 

Querying!

 
Guidance
A row detail

Detail views are divided into sections. All the information in the data section comes from columns in the selected row. The other sections display data from other, related rows.

Related data can be related in a to-one or a to-many fashion. Captions of data related in a to-many fashion link to a list view showing a filtered view of the table.

Try moving around until you find a non-empty to-many entry and click on the label to get to one. You can move back to the root by clicking on the database name in the header.

SQuiL has stopped working due to an internal error.

If you are curious you may find further information in the browser console, which is accessible through the devtools (F12).

Reload