def test_div(self, field_def, div_def):
size = np.random.random(3) + 1.0
cells = np.random.randint(8, 32, (3))
nodes = grid_filters.coordinates0_point(cells, size)
my_locals = locals() # needed for list comprehension
field = np.stack([
np.broadcast_to(eval(f, globals(), my_locals), cells)
for f in field_def
],
axis=-1)
field = field.reshape(
tuple(cells) + ((3, 3) if len(field_def) == 9 else (3, )))
div = np.stack([
np.broadcast_to(eval(c, globals(), my_locals), cells)
for c in div_def
],
axis=-1)
if len(div_def) == 3:
div = div.reshape(tuple(cells) + ((3, )))
else:
div = div.reshape(tuple(cells))
assert np.allclose(div, grid_filters.divergence(size, field))
def test_from_table_recover(self,tmp_path):
cells = np.random.randint(60,100,3)
size = np.ones(3)+np.random.rand(3)
s = seeds.from_random(size,np.random.randint(60,100))
grid = Grid.from_Voronoi_tessellation(cells,size,s)
coords = grid_filters.coordinates0_point(cells,size)
t = Table(np.column_stack((coords.reshape(-1,3,order='F'),grid.material.flatten(order='F'))),{'c':3,'m':1})
assert grid_equal(grid.sort().renumber(),Grid.from_table(t,'c',['m']))
def test_from_table(self):
cells = np.random.randint(60,100,3)
size = np.ones(3)+np.random.rand(3)
coords = grid_filters.coordinates0_point(cells,size).reshape(-1,3,order='F')
z=np.ones(cells.prod())
z[cells[:2].prod()*int(cells[2]/2):]=0
t = Table(np.column_stack((coords,z)),{'coords':3,'z':1})
t = t.add('indicator',t.get('coords')[:,0])
g = Grid.from_table(t,'coords',['indicator','z'])
assert g.N_materials == g.cells[0]*2 and (g.material[:,:,-1]-g.material[:,:,0] == cells[0]).all()
def test_from_grid_grid(self, periodic, average):
cells = np.random.randint(10, 20, 3)
size = np.ones(3) + np.random.random(3)
coords = grid_filters.coordinates0_point(cells, size).reshape(-1, 3)
np.random.shuffle(coords)
grid_1 = Grid.from_Voronoi_tessellation(cells, size, coords)
coords, material = seeds.from_grid(grid_1,
average=average,
periodic=periodic)
grid_2 = Grid.from_Voronoi_tessellation(cells, size, coords, material)
assert (grid_2.material == grid_1.material).all()
def test_curl(self,field_def,curl_def):
size = np.random.random(3)+1.0
cells = np.random.randint(8,32,(3))
nodes = grid_filters.coordinates0_point(cells,size)
my_locals = locals() # needed for list comprehension
field = np.stack([np.broadcast_to(eval(f,globals(),my_locals),cells) for f in field_def],axis=-1)
field = field.reshape(tuple(cells) + ((3,3) if len(field_def)==9 else (3,)))
curl = np.stack([np.broadcast_to(eval(c,globals(),my_locals),cells) for c in curl_def], axis=-1)
curl = curl.reshape(tuple(cells) + ((3,3) if len(curl_def)==9 else (3,)))
assert np.allclose(curl,grid_filters.curl(size,field))
def test_coordinates(self, default, mode):
if mode == 'cell':
a = grid_filters.coordinates0_point(default.cells, default.size,
default.origin)
b = default.coordinates0_point.reshape(tuple(default.cells) +
(3, ),
order='F')
elif mode == 'node':
a = grid_filters.coordinates0_node(default.cells, default.size,
default.origin)
b = default.coordinates0_node.reshape(tuple(default.cells + 1) +
(3, ),
order='F')
assert np.allclose(a, b)
def test_compare_reference_rectilinearGrid(self, update, ref_path,
tmp_path):
cells = np.array([5, 6, 7], int)
size = np.array([.6, 1., .5])
rectilinearGrid = VTK.from_rectilinear_grid(cells, size)
c = grid_filters.coordinates0_point(cells, size).reshape(-1,
3,
order='F')
n = grid_filters.coordinates0_node(cells, size).reshape(-1,
3,
order='F')
rectilinearGrid.add(c, 'cell')
rectilinearGrid.add(n, 'node')
if update:
rectilinearGrid.save(ref_path / 'rectilinearGrid')
else:
reference = VTK.load(ref_path / 'rectilinearGrid.vtr')
assert rectilinearGrid.__repr__() == reference.__repr__() and \
np.allclose(rectilinearGrid.get('cell'),c)
def test_coordinates0_point(self):
size = np.random.random(3) # noqa
cells = np.random.randint(8, 32, (3))
coord = grid_filters.coordinates0_point(cells, size)
assert np.allclose(coord[0, 0, 0], size / cells *
.5) and coord.shape == tuple(cells) + (3, )
def test_coord0(self):
size = np.random.random(3) # noqa
cells = np.random.randint(8, 32, (3))
c = grid_filters.coordinates0_point(cells + 1, size + size / cells)
n = grid_filters.coordinates0_node(cells, size) + size / cells * .5
assert np.allclose(c, n)