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feat: network_lite with minimal DiGraph class
This DiGraph class is meant to replace our use of networkx. While networkx is a great library, it loads really a lot of stuff and takes about 30MB of RAM just upon doing `import networkx`. We use maybe a hundred lines of code from this library, so let's rewrite our own directed graph class with just the algorithms we use.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,164 @@ | ||
| from collections import deque | ||
| from typing import Any, Iterable, Iterator | ||
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| class DiGraph: | ||
| """A simple directed graph class | ||
| Most functions raise KeyError if called with a node u or v not in the graph. | ||
| """ | ||
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| def __init__(self) -> None: | ||
| """Contructor, empty if no data, else load from data""" | ||
| self._edges: dict = {} | ||
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| def clear(self): | ||
| """Clear the graph""" | ||
| self._edges.clear() | ||
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| def update(self, edges: Iterable, nodes: Iterable): | ||
| """Update the graph with new edges and nodes""" | ||
| for node in nodes: | ||
| self.add_node(node) | ||
| for u, v in edges: | ||
| self.add_edge(u, v) | ||
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| def add_node(self, u): | ||
| """Add a node to the graph""" | ||
| if u not in self._edges: | ||
| self._edges[u] = [] | ||
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| def add_edge(self, u, v): | ||
| """Add a directed edge from u to v""" | ||
| self.add_node(u) | ||
| self.add_node(v) | ||
| if v not in self._edges[u]: | ||
| self._edges[u].append(v) | ||
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| def add_edges_from(self, edges: Iterable): | ||
| """Add edges from a list of tuples""" | ||
| for u, v in edges: | ||
| self.add_edge(u, v) | ||
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| @property # read-only | ||
| def nodes(self): | ||
| """Return the nodes""" | ||
| return self._edges.keys() | ||
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| @property # read-only | ||
| def edges(self) -> Iterator: | ||
| """Iterate over all edges""" | ||
| for u, neighbours in self._edges.items(): | ||
| for v in neighbours: | ||
| yield u, v | ||
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| def __contains__(self, u) -> bool: | ||
| """Check if a node is in the graph""" | ||
| return u in self._edges | ||
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| def has_path(self, u, v) -> bool: | ||
| """Check if there is a path from u to v""" | ||
| if v not in self._edges: | ||
| raise KeyError(f"Node {v} not in graph") | ||
| visited: set = set() | ||
| return self._has_path(u, v, visited) | ||
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| def _has_path(self, u, v, visited: set) -> bool: | ||
| """Helper function for has_path""" | ||
| visited.add(u) | ||
| if u == v: | ||
| return True | ||
| for neighbour in self._edges[u]: | ||
| if neighbour not in visited: | ||
| if self._has_path(neighbour, v, visited): | ||
| return True | ||
| return False | ||
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| def successors(self, u) -> Iterator: | ||
| """Return the successors of u""" | ||
| return iter(self._edges[u]) | ||
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| def descendants(self, u) -> set: | ||
| """Return the descendants of u""" | ||
| visited: set = set() | ||
| self._descendants(u, visited) | ||
| visited.remove(u) | ||
| return visited | ||
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| def _descendants(self, u, visited: set): | ||
| """Helper function for descendants""" | ||
| visited.add(u) | ||
| for neighbour in self._edges[u]: | ||
| if neighbour not in visited: | ||
| self._descendants(neighbour, visited) | ||
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| def ancestors(self, u): | ||
| """Return the ancestors of u""" | ||
| reversed_graph = DiGraph() | ||
| reversed_graph.add_edges_from((v, u) for u, v in self.edges) | ||
| for node in self.nodes: | ||
| reversed_graph.add_node(node) | ||
| return reversed_graph.descendants(u) | ||
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| def shortest_path(self, u, v) -> list: | ||
| """Return the shortest path from u to v | ||
| Algorithm: Dijsktra's algorithm for unweighted graphs, which is just BFS | ||
| Returns: | ||
| list: the shortest path from u to v | ||
| Raises: | ||
| KeyError: if u or v is not in the graph | ||
| ValueError: if there is no path from u to v | ||
| """ | ||
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| if v not in self._edges: | ||
| raise KeyError(f"Node {v} not in graph") | ||
| visited = {u: None} # dict of {node: predecessor on shortest path from u} | ||
| queue: deque[Any] = deque() | ||
| while True: | ||
| if u == v: | ||
| rev_path = [] | ||
| while u is not None: | ||
| rev_path.append(u) | ||
| u = visited[u] | ||
| return list(reversed(rev_path)) | ||
| for neighbour in self._edges[u]: | ||
| if neighbour not in visited: | ||
| visited[neighbour] = u | ||
| queue.append(neighbour) | ||
| if len(queue) == 0: | ||
| raise ValueError(f"No path from {u} to {v}") | ||
| u = queue.popleft() | ||
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| def node_link_graph(data: dict): | ||
| """Replacement for networkx.node_link_graph""" | ||
| if not data.get("directed", False): | ||
| raise ValueError("Graph must be directed") | ||
| if data.get("multigraph", True): | ||
| raise ValueError("Graph must not be a multigraph") | ||
| if not isinstance(data.get("nodes", None), list): | ||
| raise ValueError('data["nodes"] must be a list') | ||
| if not isinstance(data.get("links", None), list): | ||
| raise ValueError('data["links"] must be a list') | ||
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| graph = DiGraph() | ||
| for node in data["nodes"]: | ||
| graph.add_node(node["id"]) | ||
| for edge in data["links"]: | ||
| graph.add_edge(edge["source"], edge["target"]) | ||
| return graph | ||
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| def node_link_data(graph: DiGraph): | ||
| """Replacement for networkx.node_link_data""" | ||
| nodes = [{"id": node} for node in graph.nodes] | ||
| links = [{"source": u, "target": v} for u, v in graph.edges] | ||
| return { | ||
| "directed": True, | ||
| "graph": {}, | ||
| "links": links, | ||
| "multigraph": False, | ||
| "nodes": nodes, | ||
| } |
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