Add generic simple dijkstra method.

src/holt59/aoc/2024/day18.py

@@ -1,81 +1,58 @@
-import heapq
-from typing import Any, Iterator, TypeAlias, cast
+from typing import Any, Iterator
 
 from ..base import BaseSolver
-
-Node: TypeAlias = tuple[int, int]
-
-
-def dijkstra(
-    grid: list[Node],
-    n_rows: int,
-    n_cols: int,
-    start: Node = (0, 0),
-    target: Node | None = None,
-) -> tuple[Node, ...] | None:
-    corrupted = set(grid)
-    target = target or (n_rows - 1, n_cols - 1)
-
-    queue: list[tuple[int, Node, tuple[Node, ...]]] = [(0, start, (start,))]
-    preds: dict[Node, tuple[Node, ...]] = {}
-
-    while queue:
-        dis, node, path = heapq.heappop(queue)
-
-        if node in preds:
-            continue
-
-        preds[node] = path
-
-        if node == target:
-            break
-
-        row, col = node
-        for dr, dc in ((-1, 0), (0, 1), (1, 0), (0, -1)):
-            row_n, col_n = row + dr, col + dc
-
-            if (
-                0 <= row_n < n_rows
-                and 0 <= col_n < n_cols
-                and (row_n, col_n) not in corrupted
-                and (row_n, col_n) not in preds
-            ):
-                heapq.heappush(
-                    queue, (dis + 1, (row_n, col_n), path + ((row_n, col_n),))
-                )
-
-    return preds.get(target, None)
+from ..tools import graphs
 
 
 class Solver(BaseSolver):
     def print_grid(self, grid: list[tuple[int, int]], n_rows: int, n_cols: int):
         values = set(grid)
-        for row in range(n_rows):
-            self.logger.info(
-                "".join("#" if (row, col) in values else "." for col in range(n_cols))
+        if self.files:
+            self.files.create(
+                "graph.txt",
+                "\n".join(
+                    "".join(
+                        "#" if (row, col) in values else "." for col in range(n_cols)
+                    )
+                    for row in range(n_rows)
+                ).encode(),
+                text=True,
             )
+        else:
+            for row in range(n_rows):
+                self.logger.info(
+                    "".join(
+                        "#" if (row, col) in values else "." for col in range(n_cols)
+                    )
+                )
+
+    def dijkstra(self, corrupted: list[tuple[int, int]], n_rows: int, n_cols: int):
+        return graphs.dijkstra(
+            (0, 0),
+            (n_rows - 1, n_cols - 1),
+            graphs.make_neighbors_grid_fn(n_rows, n_cols, set(corrupted)),
+        )
 
     def solve(self, input: str) -> Iterator[Any]:
         values = [
-            cast(tuple[int, int], tuple(map(int, row.split(","))))
-            for row in input.splitlines()
+            (int(p[0]), int(p[1])) for r in input.splitlines() if (p := r.split(","))
         ]
 
-        n_rows, n_cols = (7, 7) if len(values) < 100 else (71, 71)
+        _is_test = len(values) < 100
+
+        n_rows, n_cols, n_bytes_p1 = (7, 7, 12) if _is_test else (71, 71, 1024)
 
-        n_bytes_p1 = 12 if len(values) < 100 else 1024
         bytes_p1 = values[:n_bytes_p1]
         self.print_grid(bytes_p1, n_rows, n_cols)
 
-        path_p1 = dijkstra(bytes_p1, n_rows, n_cols)
-        assert path_p1 is not None
-        yield len(path_p1) - 1
+        path_p1, cost_p1 = self.dijkstra(bytes_p1, n_rows, n_cols) or ((), -1)
+        yield cost_p1
 
         path = path_p1
         for b in range(n_bytes_p1, len(values)):
             if values[b] not in path:
                 continue
-            path = dijkstra(values[: b + 1], n_rows, n_cols)
+            path, _ = self.dijkstra(values[: b + 1], n_rows, n_cols) or (None, -1)
             if path is None:
                 yield ",".join(map(str, values[b]))
                 break

src/holt59/aoc/tools/graphs.py

@@ -0,0 +1,95 @@
+import heapq
+from typing import Callable, Iterable, TypeVar
+
+_Node = TypeVar("_Node")
+
+
+def make_neighbors_grid_fn(
+    rows: int | Iterable[int],
+    cols: int | Iterable[int],
+    excluded: Iterable[tuple[int, int]] = set(),
+    diagonals: bool = False,
+):
+    """
+    Create a neighbors function suitable for graph function for a simple grid.
+
+    Args:
+        rows: Rows of the grid. If an int is specified, the rows are assumed to be
+            numbered from 0 to rows - 1, otherwise the iterable should contain the list
+            of valid rows.
+        cols: Columns of the grid. If an int is specified, the columns are assumed to be
+            numbered from 0 to cols - 1, otherwise the iterable should contain the list
+            of valid columns.
+        excluded: Cells of the grid that cannot be used as valid nodes for the graph.
+        diagonals: If True, neighbors will include diagonal cells, otherwise, only
+            horizontal and vertical neighbors will be included.
+
+    """
+    ds = ((-1, 0), (0, 1), (1, 0), (0, -1))
+    if diagonals:
+        ds = ds + ((-1, -1), (-1, 1), (1, -1), (1, 1))
+
+    if isinstance(rows, int):
+        rows = range(rows)
+    elif not isinstance(rows, range):
+        rows = set(rows)
+
+    if isinstance(cols, int):
+        cols = range(cols)
+    elif not isinstance(cols, range):
+        cols = set(cols)
+
+    excluded = set(excluded)
+
+    def _fn(node: tuple[int, int]):
+        return (
+            ((row_n, col_n), 1)
+            for dr, dc in ds
+            if (row_n := node[0] + dr) in rows
+            and (col_n := node[1] + dc) in cols
+            and (row_n, col_n) not in excluded
+        )
+
+    return _fn
+
+
+def dijkstra(
+    start: _Node,
+    target: _Node,
+    neighbors: Callable[[_Node], Iterable[tuple[_Node, float]]],
+) -> tuple[tuple[_Node, ...], float] | None:
+    """
+    Solve shortest-path problem using simple Dijkstra algorithm from start to target,
+    using the given neighbors function.
+
+    Args:
+        start: Starting node of the path.
+        target: Target node for the path.
+        neighbors: Function that should return, for a given node, the list of
+            its neighbors with the cost to go from the node to the neighbor.
+
+    Returns:
+        One of the shortest-path from start to target with its associated cost, if one
+        is found, otherwise None.
+    """
+    queue: list[tuple[float, _Node, tuple[_Node, ...]]] = [(0, start, (start,))]
+    preds: dict[_Node, tuple[tuple[_Node, ...], float]] = {}
+
+    while queue:
+        dis, node, path = heapq.heappop(queue)
+
+        if node in preds:
+            continue
+
+        preds[node] = (path, dis)
+
+        if node == target:
+            break
+
+        for neighbor, cost in neighbors(node):
+            if neighbor in preds:
+                continue
+
+            heapq.heappush(queue, (dis + cost, neighbor, path + (neighbor,)))
+
+    return preds.get(target, None)