def single_type_ug():
"""Simple example showing how to create an unstructured grid
consisting of cells of a single type.
"""
points = array(
[
[0, 0, 0],
[1, 0, 0],
[0, 1, 0],
[0, 0, 1], # tets
[1, 0, 0],
[2, 0, 0],
[1, 1, 0],
[1, 0, 1],
[2, 0, 0],
[3, 0, 0],
[2, 1, 0],
[2, 0, 1],
],
'f')
tets = array([[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]])
tet_type = tvtk.Tetra().cell_type
ug = tvtk.UnstructuredGrid(points=points)
ug.set_cells(tet_type, tets)
return ug
def _readData(self):
from enthought.tvtk.api import tvtk
import tables
import numpy
filename = "../results/strikeslip_%s_%04dm.h5" % (shape, res)
h5 = tables.openFile(filename, 'r')
cells = h5.root.topology.cells[:]
(ncells, ncorners) = cells.shape
vertices = h5.root.geometry.vertices[:] / 1e+3
(nvertices, spaceDim) = vertices.shape
disp = h5.root.solution.snapshot0.displacements[:]
h5.close()
if shape == "tet4":
assert(spaceDim == 3)
assert(ncorners == 4)
cellType = tvtk.Tetra().cell_type
elif shape == "hex8":
assert(spaceDim == 3)
assert(ncorners == 8)
cellType = tvtk.Hexahedron().cell_type
else:
raise ValueError("Unknown shape '%s'." % shape)
data = tvtk.UnstructuredGrid()
data.points = vertices
data.set_cells(cellType, cells)
data.point_data.vectors = disp
data.point_data.vectors.name = "Displacement [m]"
return data
def _readMesh(self):
from enthought.tvtk.api import tvtk
import tables
import numpy
filename = "../meshes/reverseslip_%s_%04dm.h5" % (shape, res)
h5 = tables.openFile(filename, 'r')
cells = h5.root.topology.cells[:]
(ncells, ncorners) = cells.shape
vertices = h5.root.geometry.vertices[:] / 1e+3
(nvertices, spaceDim) = vertices.shape
h5.close()
if shape == "tet4":
assert (spaceDim == 3)
assert (ncorners == 4)
cellType = tvtk.Tetra().cell_type
else:
raise ValueError("Unknown shape '%s'." % shape)
data = tvtk.UnstructuredGrid()
data.points = vertices
data.set_cells(cellType, cells)
return data
def _read_ele_file(self):
"""
Loads data from {basename}.ele, and inserts triangle/tetrahedron cells
and cell scalars into the unstructured grid.
"""
file_name = '%s.ele' % self._basename
# Load all data.
all_data = self._get_data(file_name)
# Grab values from the first line of data file.
tet_tri, nodes_per_tet_tri, attributes = map(int, all_data[0:3])
# Reshape remainder of array.
data_array = all_data[3:].reshape(tet_tri,
1 + nodes_per_tet_tri + attributes)
nodes_array = data_array[:, 1:(nodes_per_tet_tri +
1)] - self._numbered_from
if (nodes_per_tet_tri == 3):
cell_type = tvtk.Triangle().cell_type
else:
cell_type = tvtk.Tetra().cell_type
self._grid.set_cells(cell_type, nodes_array)
for i in range(attributes):
attribute_array = data_array[:, (i + nodes_per_tet_tri +
1):(i + nodes_per_tet_tri + 2)]
self._add_attribute_array(attribute_array, i, 'cell')
def mixed_type_ug():
"""A slightly more complex example of how to generate an
unstructured grid with different cell types. Returns a created
unstructured grid.
"""
points = array(
[
[0, 0, 0],
[1, 0, 0],
[0, 1, 0],
[0, 0, 1], # tetra
[2, 0, 0],
[3, 0, 0],
[3, 1, 0],
[2, 1, 0],
[2, 0, 1],
[3, 0, 1],
[3, 1, 1],
[2, 1, 1], # Hex
],
'f')
# shift the points so we can show both.
points[:, 1] += 2.0
# The cells
cells = array([
4,
0,
1,
2,
3, # tetra
8,
4,
5,
6,
7,
8,
9,
10,
11 # hex
])
# The offsets for the cells, i.e. the indices where the cells
# start.
offset = array([0, 5])
tetra_type = tvtk.Tetra().cell_type # VTK_TETRA == 10
hex_type = tvtk.Hexahedron().cell_type # VTK_HEXAHEDRON == 12
cell_types = array([tetra_type, hex_type])
# Create the array of cells unambiguously.
cell_array = tvtk.CellArray()
cell_array.set_cells(2, cells)
# Now create the UG.
ug = tvtk.UnstructuredGrid(points=points)
# Now just set the cell types and reuse the ug locations and cells.
ug.set_cells(cell_types, offset, cell_array)
return ug
def unstructured_grid():
points = array(
[
[0, 1.2, 0.6],
[1, 0, 0],
[0, 1, 0],
[1, 1, 1], # tetra
[1, 0, -0.5],
[2, 0, 0],
[2, 1.5, 0],
[0, 1, 0],
[1, 0, 0],
[1.5, -0.2, 1],
[1.6, 1, 1.5],
[1, 1, 1], # Hex
],
'f')
# The cells
cells = array([
4,
0,
1,
2,
3, # tetra
8,
4,
5,
6,
7,
8,
9,
10,
11 # hex
])
# The offsets for the cells, i.e. the indices where the cells
# start.
offset = array([0, 5])
tetra_type = tvtk.Tetra().cell_type # VTK_TETRA == 10
hex_type = tvtk.Hexahedron().cell_type # VTK_HEXAHEDRON == 12
cell_types = array([tetra_type, hex_type])
# Create the array of cells unambiguously.
cell_array = tvtk.CellArray()
cell_array.set_cells(2, cells)
# Now create the UG.
ug = tvtk.UnstructuredGrid(points=points)
# Now just set the cell types and reuse the ug locations and cells.
ug.set_cells(cell_types, offset, cell_array)
scalars = random.random(points.shape[0])
ug.point_data.scalars = scalars
ug.point_data.scalars.name = 'scalars'
return ug
def make_data():
points = N.array(
[
[0, 0, 0],
[1, 0, 0],
[0, 1, 0],
[0, 0, 1], # tets
[1, 0, 0],
[2, 0, 0],
[1, 1, 0],
[1, 0, 1],
[2, 0, 0],
[3, 0, 0],
[2, 1, 0],
[2, 0, 1],
],
'f')
tets = N.array([[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]])
tet_type = tvtk.Tetra().cell_type
ug = tvtk.UnstructuredGrid(points=points)
ug.set_cells(tet_type, tets)
# Setup the point attributes.
temp = N.random.random(12)
v = N.random.randn(12, 3)
ten = N.random.randn(12, 9)
a = tvtk.FloatArray(name='p')
a.from_array(N.random.randn(12))
ug.point_data.add_array(a)
ug.point_data.scalars = temp
ug.point_data.scalars.name = 't'
ug.point_data.vectors = v
ug.point_data.vectors.name = 'v'
ug.point_data.tensors = ten
ug.point_data.tensors.name = 'ten'
# Setup the cell attributes.
temp = N.random.random(3)
v = N.random.randn(3, 3)
ten = N.random.randn(3, 9)
ug.cell_data.scalars = temp
ug.cell_data.scalars.name = 't'
ug.cell_data.vectors = v
ug.cell_data.vectors.name = 'v'
ug.cell_data.tensors = ten
ug.cell_data.tensors.name = 'ten'
return ug
def _readData(self):
from enthought.tvtk.api import tvtk
import tables
import numpy
filename = "../projection/pylith_%s_%04dm.h5" % (shape, res)
h5 = tables.openFile(filename, 'r')
cells = h5.root.topology.cells[:]
(ncells, ncorners) = cells.shape
vertices = h5.root.geometry.vertices[:] / 1e+3
(nvertices, spaceDim) = vertices.shape
error = h5.root.difference.pylith_1_0_analytic.snapshot0.displacements[:]
disp = h5.root.projection.data.pylith_1_0.snapshot0.displacements[:]
dispE = h5.root.projection.data.analytic.snapshot0.displacements[:]
h5.close()
if shape == "tet4":
assert(spaceDim == 3)
assert(ncorners == 4)
cellType = tvtk.Tetra().cell_type
elif shape == "hex8":
assert(spaceDim == 3)
assert(ncorners == 8)
cellType = tvtk.Hexahedron().cell_type
else:
raise ValueError("Unknown shape '%s'." % shape)
errorLog10 = numpy.log10(error)
data = tvtk.UnstructuredGrid()
data.points = vertices
data.set_cells(cellType, cells)
data.cell_data.scalars = errorLog10
data.cell_data.scalars.name = "log10(Error [m])"
return data
def mshplot3D(mesh, scale=[1, 1, 1], elm_ids=[], labels=[0, 0, 0]):
''' Plot 3D meshes (and optionally label vertices, elements, and edges)
@param mesh A 3D mesh object
@param elm_ids Numpy array of elements that will be plotted. If left empty,
all elements are plotted.
@param labels (\c bool) List of three elements indicateting what should be
labeled. By default, nothing is labeled
labels[0]=True labels vertices
labels[1]=True labels elements
labels[2]=True labels edges
@param scale (\c int) List indicating the scaling of the vertices -- this
is used to scale the z-direction for a thin mesh. By
default, there is no scaling, i.e. scale = [1, 1, 1]
@see msh.mesh.Mesh3D
@author Matt Ueckermann
'''
if type(elm_ids) is not int:
if len(elm_ids) == 0:
elm_ids = np.arange(len(mesh.elm))
#Figure out how many vertices are in each element type
n_vert_in_type = [len(int_el_pqr(element=element, dim=3)[0]) \
for element in mesh.u_elm_type]
#Now create the TVTK data-structures for the 'cells' and 'offsets'
#cells give [#verts, vert1id, vert2id...,vertnid, #verts ...]
#offsets gives the id's in the cells list where #verts are listed. So above
# it is [0, n+1]
cells = np.array([], dtype=int)
offset = np.array([], dtype=int)
for elm, elm_type in zip(mesh.elm[elm_ids, :], mesh.elm_type[elm_ids]):
n_vt = n_vert_in_type[elm_type]
offset = np.append(offset, len(cells))
cells = np.append(cells, [n_vt] + elm[:n_vt].tolist())
#Also have to create a list of element-types -- to do that we have to
#convert from my numbering system to the TVTK numbering system
if mesh.dim == 3:
type_convert = np.array([tvtk.Tetra().cell_type,\
tvtk.Hexahedron().cell_type, tvtk.Wedge().cell_type])
elif mesh.dim == 2:
type_convert = np.array([tvtk.Triangle().cell_type,\
tvtk.Quad().cell_type])
cell_types = mesh.u_elm_type[mesh.elm_type[elm_ids]]
cell_types = type_convert[cell_types]
#To help visualize the mesh, we color it be the distance from the origin
if mesh.dim == 3:
x, y, z = mesh.vert[:].T
elif mesh.dim == 2:
x, y = mesh.vert[:, :2].T
z = np.zeros_like(x)
dist = np.sqrt(x**2 + y**2 + z**2)
#and we scale the x, y, z, coordinates according the the assigned 'scale'
points = np.column_stack((x * scale[0], y * scale[1], z * scale[2]))
#Now we create the data-structures
cell_array = tvtk.CellArray()
cell_array.set_cells(len(offset), cells)
ug = tvtk.UnstructuredGrid(points=points)
ug.set_cells(cell_types, offset, cell_array)
ug.point_data.scalars = dist.ravel()
ug.point_data.scalars.name = 'Distance from Origin'
#Next we set the new data-structures as a mayavi source
src = sources.vtk_data_source.VTKDataSource(data=ug)
#Extract the edges from the grid
edges = mlab.pipeline.extract_edges(src)
#Use a shorter name for the elements
elm = mesh.elm[elm_ids, :]
if any(labels) and len(elm) > 20:
string = "WARNING:\nAre you sure you want to label more than 20" + \
"elements in 3D? -- Labels are very memory" + \
"(apparently -- who would have guessed?) \nY(es) to proceed:"
answer = raw_input(string)
if answer.lower() not in ['yes', 'y', 'yes']:
labels = [0, 0, 0]
#Next add labels if desired:
maxdist = np.max(dist) / 2.
if labels[0]: #Vertex labels
for i in xrange(len(offset)):
for vnum in elm[i, :]:
if vnum >= 0:
mlab.text3d(points[vnum, 0], points[vnum, 1], \
points[vnum, 2], '%d' % vnum, \
color=(0, 0, 0), scale=0.02*maxdist)
if labels[1]: #element labels
for i in xrange(len(offset)):
if elm_ids[i] < 0:
elm_label = mesh.numelm + elm_ids[i]
else:
elm_label = elm_ids[i]
x = np.mean(points[elm[i, :cells[offset[i]]], 0])
y = np.mean(points[elm[i, :cells[offset[i]]], 1])
z = np.mean(points[elm[i, :cells[offset[i]]], 2])
mlab.text3d(x, y, z, '%d' % elm_label, color=(0.3, 0.3, 0.9), \
scale=0.03*maxdist)
if labels[2]: #edge labels
if len(elm_ids) < len(mesh.elm):
elm2ed = connect_elm2ed(mesh.elm2elm[:], mesh.ed2ed)
ed_ids = np.unique(elm2ed[elm_ids])
ed_ids = ed_ids[ed_ids >= 0]
ed2ed = mesh.ed2ed[ed_ids, :]
else:
ed_ids = np.arange(len(mesh.ed2ed))
ed2ed = mesh.ed2ed[:]
for i in xrange(len(ed2ed)):
n = 8
if ed2ed[i, -1] < 0:
n = 7
x = np.mean(points[ed2ed[i, 4:n], 0])
y = np.mean(points[ed2ed[i, 4:n], 1])
z = np.mean(points[ed2ed[i, 4:n], 2])
mlab.text3d(x, y, z, '%d' % ed_ids[i], color=(0.3, 0.9, 0.3), \
scale=0.03*maxdist)
#And plot them!
mlab.pipeline.surface(edges, opacity=0.4, line_width=2)
mlab.axes()
mlab.title('3D mesh', size=0.5, height=0.95)
#mlab.show()
return cells, offset, cell_types