def slice_traverse(self, viewpoint=None):
if not hasattr(self.ds.index, "grid"):
raise NotImplementedError
for node in kd_traverse(self.tree.trunk, viewpoint=viewpoint):
grid = self.ds.index.grids[node.grid - self._id_offset]
dds = grid.dds
gle = grid.LeftEdge.in_units("code_length").ndarray_view()
nle = get_left_edge(node)
nre = get_right_edge(node)
li = np.rint((nle - gle) / dds).astype('int32')
ri = np.rint((nre - gle) / dds).astype('int32')
dims = (ri - li).astype('int32')
sl = (slice(li[0], ri[0]), slice(li[1],
ri[1]), slice(li[2], ri[2]))
gi = grid.get_global_startindex() + li
yield grid, node, (sl, dims, gi)
def sum_cells(self, all_cells=False):
cells = 0
for node in depth_traverse(self.trunk):
if node.grid == -1:
continue
if not all_cells and not kd_is_leaf(node):
continue
grid = self.ds.index.grids[node.grid - self._id_offset]
dds = grid.dds
gle = grid.LeftEdge
nle = self.ds.arr(get_left_edge(node), input_units="code_length")
nre = self.ds.arr(get_right_edge(node), input_units="code_length")
li = np.rint((nle - gle) / dds).astype('int32')
ri = np.rint((nre - gle) / dds).astype('int32')
dims = (ri - li).astype('int32')
cells += np.prod(dims)
return cells
def slice_traverse(self, viewpoint = None):
if not hasattr(self.ds.index, "grid"):
raise NotImplementedError
for node in kd_traverse(self.tree.trunk, viewpoint=viewpoint):
grid = self.ds.index.grids[node.grid - self._id_offset]
dds = grid.dds
gle = grid.LeftEdge.in_units("code_length").ndarray_view()
nle = get_left_edge(node)
nre = get_right_edge(node)
li = np.rint((nle-gle)/dds).astype('int32')
ri = np.rint((nre-gle)/dds).astype('int32')
dims = (ri - li).astype('int32')
sl = (slice(li[0], ri[0]),
slice(li[1], ri[1]),
slice(li[2], ri[2]))
gi = grid.get_global_startindex() + li
yield grid, node, (sl, dims, gi)
def sum_cells(self, all_cells=False):
cells = 0
for node in depth_traverse(self.trunk):
if node.grid == -1:
continue
if not all_cells and not kd_is_leaf(node):
continue
grid = self.ds.index.grids[node.grid - self._id_offset]
dds = grid.dds
gle = grid.LeftEdge
nle = self.ds.arr(get_left_edge(node), input_units="code_length")
nre = self.ds.arr(get_right_edge(node), input_units="code_length")
li = np.rint((nle-gle)/dds).astype('int32')
ri = np.rint((nre-gle)/dds).astype('int32')
dims = (ri - li).astype('int32')
cells += np.prod(dims)
return cells
def get_brick_data(self, node):
if node.data is not None: return node.data
grid = self.ds.index.grids[node.grid - self._id_offset]
dds = grid.dds.ndarray_view()
gle = grid.LeftEdge.ndarray_view()
nle = get_left_edge(node)
nre = get_right_edge(node)
li = np.rint((nle - gle) / dds).astype('int32')
ri = np.rint((nre - gle) / dds).astype('int32')
dims = (ri - li).astype('int32')
assert (np.all(grid.LeftEdge <= nle))
assert (np.all(grid.RightEdge >= nre))
if grid in self.current_saved_grids:
dds = self.current_vcds[self.current_saved_grids.index(grid)]
else:
dds = []
for i, field in enumerate(self.fields):
vcd = grid.get_vertex_centered_data(
field, smoothed=True,
no_ghost=self.no_ghost).astype('float64')
if self.log_fields[i]: vcd = np.log10(vcd)
dds.append(vcd)
self.current_saved_grids.append(grid)
self.current_vcds.append(dds)
if self.data_source.selector is None:
mask = np.ones(dims, dtype='uint8')
else:
mask = self.data_source.selector.fill_mask(grid)[
li[0]:ri[0], li[1]:ri[1], li[2]:ri[2]].astype('uint8')
data = [
d[li[0]:ri[0] + 1, li[1]:ri[1] + 1, li[2]:ri[2] + 1].copy()
for d in dds
]
brick = PartitionedGrid(grid.id, data, mask, nle.copy(), nre.copy(),
dims.astype('int64'))
node.data = brick
if not self._initialized:
self.brick_dimensions.append(dims)
return brick
def get_brick_data(self, node):
if node.data is not None: return node.data
grid = self.ds.index.grids[node.grid - self._id_offset]
dds = grid.dds.ndarray_view()
gle = grid.LeftEdge.ndarray_view()
nle = get_left_edge(node)
nre = get_right_edge(node)
li = np.rint((nle-gle)/dds).astype('int32')
ri = np.rint((nre-gle)/dds).astype('int32')
dims = (ri - li).astype('int32')
assert(np.all(grid.LeftEdge <= nle))
assert(np.all(grid.RightEdge >= nre))
if grid in self.current_saved_grids:
dds = self.current_vcds[self.current_saved_grids.index(grid)]
else:
dds = []
for i, field in enumerate(self.fields):
vcd = grid.get_vertex_centered_data(field, smoothed=True, no_ghost=self.no_ghost).astype('float64')
if self.log_fields[i]: vcd = np.log10(vcd)
dds.append(vcd)
self.current_saved_grids.append(grid)
self.current_vcds.append(dds)
if self.data_source.selector is None:
mask = np.ones(dims, dtype='uint8')
else:
mask = self.data_source.selector.fill_mask(grid)[li[0]:ri[0], li[1]:ri[1], li[2]:ri[2] ].astype('uint8')
data = [d[li[0]:ri[0]+1,
li[1]:ri[1]+1,
li[2]:ri[2]+1].copy() for d in dds]
brick = PartitionedGrid(grid.id, data,
mask,
nle.copy(),
nre.copy(),
dims.astype('int64'))
node.data = brick
if not self._initialized:
self.brick_dimensions.append(dims)
return brick
def check_tree(self):
for node in depth_traverse(self.trunk):
if node.grid == -1:
continue
grid = self.ds.index.grids[node.grid - self._id_offset]
dds = grid.dds
gle = grid.LeftEdge
nle = self.ds.arr(get_left_edge(node), input_units="code_length")
nre = self.ds.arr(get_right_edge(node), input_units="code_length")
li = np.rint((nle - gle) / dds).astype('int32')
ri = np.rint((nre - gle) / dds).astype('int32')
dims = (ri - li).astype('int32')
assert (np.all(grid.LeftEdge <= nle))
assert (np.all(grid.RightEdge >= nre))
assert (np.all(dims > 0))
# print grid, dims, li, ri
# Calculate the Volume
vol = kd_sum_volume(self.trunk)
mylog.debug('AMRKDTree volume = %e' % vol)
kd_node_check(self.trunk)
def check_tree(self):
for node in depth_traverse(self.trunk):
if node.grid == -1:
continue
grid = self.ds.index.grids[node.grid - self._id_offset]
dds = grid.dds
gle = grid.LeftEdge
nle = self.ds.arr(get_left_edge(node), input_units="code_length")
nre = self.ds.arr(get_right_edge(node), input_units="code_length")
li = np.rint((nle-gle)/dds).astype('int32')
ri = np.rint((nre-gle)/dds).astype('int32')
dims = (ri - li).astype('int32')
assert(np.all(grid.LeftEdge <= nle))
assert(np.all(grid.RightEdge >= nre))
assert(np.all(dims > 0))
# print grid, dims, li, ri
# Calculate the Volume
vol = kd_sum_volume(self.trunk)
mylog.debug('AMRKDTree volume = %e' % vol)
kd_node_check(self.trunk)
def test_amr_kdtree_coverage():
return #TESTDISABLED
domain_dims = (32, 32, 32)
data = np.zeros(domain_dims) + 0.25
fo = [
ic.CoredSphere(0.05, 0.3, [0.7, 0.4, 0.75], {"density": (0.25, 100.0)})
]
rc = [fm.flagging_method_registry["overdensity"](8.0)]
ug = load_uniform_grid({"density": data}, domain_dims, 1.0)
ds = refine_amr(ug, rc, fo, 5)
kd = AMRKDTree(ds)
volume = kd.count_volume()
yield assert_equal, volume, \
np.prod(ds.domain_right_edge - ds.domain_left_edge)
cells = kd.count_cells()
true_cells = ds.all_data().quantities['TotalQuantity']('Ones')[0]
yield assert_equal, cells, true_cells
# This largely reproduces the AMRKDTree.tree.check_tree() functionality
tree_ok = True
for node in depth_traverse(kd.tree.trunk):
if node.grid is None:
continue
grid = ds.index.grids[node.grid - kd._id_offset]
dds = grid.dds
gle = grid.LeftEdge
nle = get_left_edge(node)
nre = get_right_edge(node)
li = np.rint((nle - gle) / dds).astype('int32')
ri = np.rint((nre - gle) / dds).astype('int32')
dims = (ri - li).astype('int32')
tree_ok *= np.all(grid.LeftEdge <= nle)
tree_ok *= np.all(grid.RightEdge >= nre)
tree_ok *= np.all(dims > 0)
yield assert_equal, True, tree_ok
def test_amr_kdtree_coverage():
return #TESTDISABLED
domain_dims = (32, 32, 32)
data = np.zeros(domain_dims) + 0.25
fo = [ic.CoredSphere(0.05, 0.3, [0.7, 0.4, 0.75],
{"density": (0.25, 100.0)})]
rc = [fm.flagging_method_registry["overdensity"](8.0)]
ug = load_uniform_grid({"density": data}, domain_dims, 1.0)
ds = refine_amr(ug, rc, fo, 5)
kd = AMRKDTree(ds)
volume = kd.count_volume()
yield assert_equal, volume, \
np.prod(ds.domain_right_edge - ds.domain_left_edge)
cells = kd.count_cells()
true_cells = ds.all_data().quantities['TotalQuantity']('Ones')[0]
yield assert_equal, cells, true_cells
# This largely reproduces the AMRKDTree.tree.check_tree() functionality
tree_ok = True
for node in depth_traverse(kd.tree.trunk):
if node.grid is None:
continue
grid = ds.index.grids[node.grid - kd._id_offset]
dds = grid.dds
gle = grid.LeftEdge
nle = get_left_edge(node)
nre = get_right_edge(node)
li = np.rint((nle-gle)/dds).astype('int32')
ri = np.rint((nre-gle)/dds).astype('int32')
dims = (ri - li).astype('int32')
tree_ok *= np.all(grid.LeftEdge <= nle)
tree_ok *= np.all(grid.RightEdge >= nre)
tree_ok *= np.all(dims > 0)
yield assert_equal, True, tree_ok
