Dynamic Discrete Spaces #2754
Description
Currently, all DiscreteSpace subclasses rely on @cache and @cached_property for neighborhood-related functionality. This is critical for performance. However, if you want to represent a dynamically changing discrete space (e.g., a dynamic network), this is currently not possible.
It seems that functools offers a clear_cache function that can be called on anything decorated with a caching decorator. This means that it is possible to retain the performance benefits of caching while also being able to clear it on specific parts of the DiscreteSpace.
Something like the following is roughly what would be needed. Note that this is not tested and just quickly put together based on some docs.
class DynamicNetwork(Network):
def add_cell(cell:Cell):
self.G.add_node(cell.coordinate)
self._cells[cell.coordinate] = cell.coordinate
def add_edge(cell1:Cell, cell2:Cell):
self.G.add_edge(cell1.coordinate, cell2.coordinate)
for cell in [cell1, cell2]:
cell.get_neighborhood.cache_clear()
cell.neigbhoorhood.cache_clear()
cell._neighborhood.cache_clear()
self._connect_single_cell(cell)
def remove_cell(cell:Cell):
neigbors = cell.neighborhood
self.G.remove_node(cell.coordinate)
# iterate over all neighbors
for cell in neighbors:
cell.get_neighborhood.cache_clear()
cell.neigbhoorhood.cache_clear()
cell._neighborhood.cache_clear()
self._connect_single_cell(cell)
def remove_edge(cell1:Cell, cell2:Cell):
self.G.remove_edge(cell1.coordinate, cell2.coordinate)
for cell in [cell1, cell2]:
cell.get_neighborhood.cache_clear()
cell.neigbhoorhood.cache_clear()
cell._neighborhood.cache_clear()
self._connect_single_cell(cell)