Merge branch 'main' into feat/improving-components-api

src/ansys/mapdl/core/post.py

@@ -635,7 +635,7 @@ def _plot_cell_scalars(self, scalars, show_elem_numbering=False, **kwargs):
         # as ``disp`` returns the result for all nodes/elems, we need all node/elem numbers
         # and to index to the output node numbers
         if hasattr(self._mapdl.mesh, "enum_all"):
-            enum = self._mapdl.mesh.enum
+            enum = self._mapdl.mesh.enum_all
         else:
             enum = self._all_enum
 
@@ -666,30 +666,22 @@ def _plot_cell_scalars(self, scalars, show_elem_numbering=False, **kwargs):
         # The theoretical approach will be using an intermediate array of the
         # size of the MAPDL total number of elements (we do not care about selected).
         #
-        # >>> values = np.zeros(enum.shape)
+        values = np.zeros(enum.shape)
         #
         # Then assign the MAPDL values for the selected element (scalars)
         #
-        # >>> values[self.selected_elements] = scalars
+        values[self.selected_elements] = scalars
         #
         # Because values are in order, but with python notation, then we can do:
         #
-        # >>> surf_values = values[uni-1]  # -1 to set MAPDL element indexing to python indexing
-        #
-        # To obtain the surf values of the selected elements in the MAPDL notation
+        surf_values = values[
+            uni - 1
+        ]  # -1 to set MAPDL element indexing to python indexing
         #
         # Then to account for the original Pyvista object:
         #
-        # >>> surf_values = surf_values[ridx]
+        surf_values = surf_values[ridx]
         #
-        # Effective approach
-        # ------------------
-        # Now the more efficient approach (not allocating a full size array):
-        # values = np.zeros(enum[self.selected_elements].shape)
-        # values = scalars # But this is the scalars itself! Hence, putting all
-        # together:
-        surf_values = scalars[uni - 1][ridx]
-
         #######################################################################
 
         meshes = [

tests/test_post.py

@@ -765,13 +765,44 @@ def test_plot_incomplete_element_selection(mapdl, contact_solve):
     mapdl.esel("S", "ELEM", "", 1, mapdl.mesh.n_elem // 2)
     assert mapdl.post_processing.plot_element_displacement() is None
 
+    mapdl.nsel("S", "NODE", "", 1, mapdl.mesh.n_elem // 2, 2)
+    assert mapdl.post_processing.plot_element_displacement() is None
+
+    mapdl.nsel("S", "NODE", "", 5, mapdl.mesh.n_elem // 2, 2)
+    assert mapdl.post_processing.plot_element_displacement() is None
+
+    mapdl.vsel("s", "", "", 1)
+    mapdl.eslv("s")
+    assert mapdl.post_processing.plot_element_displacement() is None
+
+    mapdl.vsel("s", "", "", 2)
+    mapdl.eslv("s")
+    assert mapdl.post_processing.plot_element_displacement() is None
+
 
 @requires("pyvista")
-@pytest.mark.xfail(strict=False, reason="The image regression is failing. See #2435")
-def test_plot_incomplete_nodal_selection(mapdl, contact_solve):
+def test_plot_incomplete_nodal_selection(mapdl, contact_solve, verify_image_cache):
+    verify_image_cache.skip = True
+
     mapdl.nsel("S", "NODE", "", 1, mapdl.mesh.n_node // 2)
     assert mapdl.post_processing.plot_nodal_displacement() is None
 
+    mapdl.nsel("S", "NODE", "", 1, mapdl.mesh.n_node // 2, 2)
+    assert mapdl.post_processing.plot_nodal_displacement() is None
+
+    mapdl.nsel("S", "NODE", "", 5, mapdl.mesh.n_node // 2, 2)
+    assert mapdl.post_processing.plot_nodal_displacement() is None
+
+    mapdl.vsel("s", "", "", 1)
+    mapdl.eslv("S")
+    mapdl.nsle("S")
+    assert mapdl.post_processing.plot_nodal_displacement() is None
+
+    mapdl.vsel("s", "", "", 2)
+    mapdl.eslv("S")
+    mapdl.nsle("S")
+    assert mapdl.post_processing.plot_nodal_displacement() is None
+
 
 @requires("pyvista")
 def test_general_plotter_returns(mapdl, static_solve, verify_image_cache):