Deep refactor

ngraph/lib/algorithms/base.py

@@ -0,0 +1,64 @@
+from __future__ import annotations
+
+from enum import IntEnum
+from typing import Union, Tuple
+from ngraph.lib.graph import NodeID, EdgeID
+
+#: Represents numeric cost in the network (e.g. distance, latency, etc.).
+Cost = Union[int, float]
+
+#: A single path element is a tuple of:
+#:   - The current node ID.
+#:   - A tuple of one or more parallel edge IDs from this node to the next node.
+#: In a complete path, intermediate elements usually have a non-empty edge tuple,
+#: while the final element has an empty tuple to indicate termination.
+PathElement = Tuple[NodeID, Tuple[EdgeID]]
+
+#: A path is a tuple of PathElements forming a complete route from
+#: a source node to a destination node.
+PathTuple = Tuple[PathElement, ...]
+
+#: Capacity threshold below which capacity values are treated as effectively zero.
+MIN_CAP = 2**-12
+
+#: Flow threshold below which flow values are treated as effectively zero.
+MIN_FLOW = 2**-12
+
+
+class PathAlg(IntEnum):
+    """
+    Types of path finding algorithms
+    """
+
+    SPF = 1
+    KSP_YENS = 2
+
+
+class EdgeSelect(IntEnum):
+    """
+    Edge selection criteria determining which edges are considered
+    for path-finding between a node and its neighbor(s).
+    """
+
+    #: Return all edges matching the minimum metric among the candidate edges.
+    ALL_MIN_COST = 1
+    #: Return all edges matching the minimum metric among edges with remaining capacity.
+    ALL_MIN_COST_WITH_CAP_REMAINING = 2
+    #: Return all edges that have remaining capacity, ignoring metric except for returning min_cost.
+    ALL_ANY_COST_WITH_CAP_REMAINING = 3
+    #: Return exactly one edge (the single lowest metric).
+    SINGLE_MIN_COST = 4
+    #: Return exactly one edge, the lowest-metric edge with remaining capacity.
+    SINGLE_MIN_COST_WITH_CAP_REMAINING = 5
+    #: Return exactly one edge factoring both metric and load:
+    #: cost = (metric * 100) + round(flow / capacity * 10).
+    SINGLE_MIN_COST_WITH_CAP_REMAINING_LOAD_FACTORED = 6
+    #: Use a user-defined function for edge selection logic.
+    USER_DEFINED = 99
+
+
+class FlowPlacement(IntEnum):
+    """Ways to distribute flow on parallel edges."""
+
+    PROPORTIONAL = 1  # Flow is split proportional to capacity (Dinic-like approach)
+    EQUAL_BALANCED = 2  # Flow is equally divided among parallel edges