def generate_and_annotations(self, fig: FluidInteractionGraph) -> None:
m = self._connectivity_matrix
shape = m.shape
n_cols = shape[1]
# Find all the different columns which are equal
all_candidates = []
skip_list = []
for i in range(n_cols):
candidate = []
candidate.append(self.get_fig(i))
found_flag = False
if i in skip_list:
continue
for j in range(i + 1, n_cols):
if j in skip_list:
continue
col_i = m[:, i]
col_j = m[:, j]
if np.array_equal(col_i, col_j):
skip_list.append(i)
skip_list.append(j)
candidate.append(self.get_edge(j))
found_flag = True
if found_flag is True:
all_candidates.append(candidate)
print(all_candidates)
# Generate all the different AND annotations necessary
for candidate in all_candidates:
for edge in candidate:
# TODO - Figure out if the edge needs any additional markup here
source_node = fig.get_fignode(edge[0])
target_node = fig.get_fignode(edge[1])
if source_node is None:
raise Exception(
"Could not find the corresponding nodes {}".format(
source_node))
if target_node is None:
raise Exception(
"could not find the corresponding nodes {}".format(
target_node))
fig.connect_fignodes(source_node, target_node)
origin_nodes = [fig.get_fignode(edge[0]) for edge in candidate]
print("Added AND annotation on FIG: {}".format(str(origin_nodes)))
assert (origin_nodes is not None)
annotation = fig.add_and_annotation(origin_nodes)
self._and_annotations.append(annotation)
def generate_and_annotations(self, fig: FluidInteractionGraph) -> None:
m = self._connectivity_matrix
shape = m.shape
n_cols = shape[1]
# Find all the different columns which are equal
all_candidates = []
skip_list = []
for i in range(n_cols):
candidate = []
candidate.append(self.get_connectivity_edge(i))
found_flag = False
if i in skip_list:
continue
for j in range(i + 1, n_cols):
if j in skip_list:
continue
col_i = m[:, i]
col_j = m[:, j]
if np.array_equal(col_i, col_j):
skip_list.append(i)
skip_list.append(j)
candidate.append(self.get_connectivity_edge(j))
found_flag = True
if found_flag is True:
all_candidates.append(candidate)
print("DISTRIBUTE-AND CANDIDATES:")
print(all_candidates)
# Populating this skip list is important to make sure
# that all the nodes with the AND annotation are zero'ed
# This will simplify how the or-compuation can be done
for candidate in all_candidates:
# Skip the first one and add the rest of
# the edges into the skip list
for i in range(1, len(candidate)):
self.add_to_column_skip_list(candidate[i])
# Add all the annotated edges to the list that keeps
# track of used edges, this way we can ensrue that all
# used edges are accounted for when we need to use the
# NOTAnnotation
for candidate in all_candidates:
self._annotated_connectivity_edges.extend(candidate)
# Generate all the different AND annotations necessary
for candidate in all_candidates:
for edge in candidate:
# TODO - Figure out if the edge needs any additional markup here
source_node = fig.get_fignode(edge[0])
target_node = fig.get_fignode(edge[1])
if source_node is None:
raise Exception(
"Could not find the corresponding nodes {}".format(
source_node))
if target_node is None:
raise Exception(
"could not find the corresponding nodes {}".format(
target_node))
fig.connect_fignodes(source_node, target_node)
origin_nodes = [fig.get_fignode(edge[0]) for edge in candidate]
print("Added AND annotation on FIG: {}".format(str(origin_nodes)))
assert origin_nodes is not None
annotation = fig.add_and_annotation(origin_nodes)
self._and_annotations.append(annotation)