def test_single_scalar(self):
grid = Grid(shape=(4, 4))
f = Function(name='f', grid=grid)
assign(f, 4)
assert np.all(f.data == 4)
def test_multiple_fns_single_scalar(self):
grid = Grid(shape=(4, 4))
f = Function(name='f', grid=grid)
g = Function(name='g', grid=grid)
h = Function(name='h', grid=grid)
functions = [f, g, h]
assign(functions, 2)
assert np.all(f.data == 2)
assert np.all(g.data == 2)
assert np.all(h.data == 2)
def test_assign_parallel(self):
a = np.arange(64).reshape((8, 8))
grid = Grid(shape=a.shape)
f = Function(name='f', grid=grid, dtype=np.int32)
f.data[:] = a
g = Function(name='g', grid=grid, dtype=np.int32)
assign(g, f)
loc_shape = np.array(grid._distributor.shape)
loc_coords = np.array(grid._distributor.mycoords)
start = loc_shape * loc_coords
stop = loc_shape * (loc_coords + 1)
slices = []
for i, j in zip(start, stop):
slices.append(slice(i, j, 1))
slices = as_tuple(slices)
assert np.all(a[slices] - np.array(g.data[:]) == 0)
def wrapper(self):
if self._data is None:
debug("Allocating memory for %s%s" % (self.name, self.shape_allocated))
self._data = Data(self.shape_allocated, self.dtype,
modulo=self._mask_modulo, allocator=self._allocator)
if self._first_touch:
assign(self, 0)
if callable(self._initializer):
if self._first_touch:
warning("`first touch` together with `initializer` causing "
"redundant data initialization")
try:
self._initializer(self.data_with_halo)
except ValueError:
# Perhaps user only wants to initialise the physical domain
self._initializer(self.data)
else:
self.data_with_halo.fill(0)
return func(self)
def test_equations_with_options(self):
class CompDomain(SubDomain):
name = 'comp_domain'
def define(self, dimensions):
return {d: ('middle', 1, 1) for d in dimensions}
comp_domain = CompDomain()
grid = Grid(shape=(4, 4), subdomains=comp_domain)
f = Function(name='f', grid=grid)
g = Function(name='g', grid=grid)
functions = [f, g]
scalars = 2
options = [None, {'subdomain': grid.subdomains['comp_domain']}]
assign(functions, scalars, options=options)
assert np.all(f.data == 2)
assert np.all(
np.array(g.data) == [[0, 0, 0, 0], [0, 2, 2, 0], [0, 2, 2, 0],
[0, 0, 0, 0]])
