Remove hardcoded libpython binaries and add debug step

venv/lib/python3.12/site-packages/altgraph/GraphAlgo.py

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+"""
+altgraph.GraphAlgo - Graph algorithms
+=====================================
+"""
+from altgraph import GraphError
+
+
+def dijkstra(graph, start, end=None):
+    """
+    Dijkstra's algorithm for shortest paths
+
+    `David Eppstein, UC Irvine, 4 April 2002
+        <http://www.ics.uci.edu/~eppstein/161/python/>`_
+
+    `Python Cookbook Recipe
+        <http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/119466>`_
+
+    Find shortest paths from the  start node to all nodes nearer than or
+    equal to the end node.
+
+    Dijkstra's algorithm is only guaranteed to work correctly when all edge
+    lengths are positive.  This code does not verify this property for all
+    edges (only the edges examined until the end vertex is reached), but will
+    correctly compute shortest paths even for some graphs with negative edges,
+    and will raise an exception if it discovers that a negative edge has
+    caused it to make a mistake.
+
+    Adapted to altgraph by Istvan Albert, Pennsylvania State University -
+    June, 9 2004
+    """
+    D = {}  # dictionary of final distances
+    P = {}  # dictionary of predecessors
+    Q = _priorityDictionary()  # estimated distances of non-final vertices
+    Q[start] = 0
+
+    for v in Q:
+        D[v] = Q[v]
+        if v == end:
+            break
+
+        for w in graph.out_nbrs(v):
+            edge_id = graph.edge_by_node(v, w)
+            vwLength = D[v] + graph.edge_data(edge_id)
+            if w in D:
+                if vwLength < D[w]:
+                    raise GraphError(
+                        "Dijkstra: found better path to already-final vertex"
+                    )
+            elif w not in Q or vwLength < Q[w]:
+                Q[w] = vwLength
+                P[w] = v
+
+    return (D, P)
+
+
+def shortest_path(graph, start, end):
+    """
+    Find a single shortest path from the *start* node to the *end* node.
+    The input has the same conventions as dijkstra(). The output is a list of
+    the nodes in order along the shortest path.
+
+    **Note that the distances must be stored in the edge data as numeric data**
+    """
+
+    D, P = dijkstra(graph, start, end)
+    Path = []
+    while 1:
+        Path.append(end)
+        if end == start:
+            break
+        end = P[end]
+    Path.reverse()
+    return Path
+
+
+#
+# Utility classes and functions
+#
+class _priorityDictionary(dict):
+    """
+    Priority dictionary using binary heaps (internal use only)
+
+    David Eppstein, UC Irvine, 8 Mar 2002
+
+    Implements a data structure that acts almost like a dictionary, with
+    two modifications:
+
+        1. D.smallest() returns the value x minimizing D[x].  For this to
+           work correctly, all values D[x] stored in the dictionary must be
+           comparable.
+
+        2. iterating "for x in D" finds and removes the items from D in sorted
+           order. Each item is not removed until the next item is requested,
+           so D[x] will still return a useful value until the next iteration
+           of the for-loop.  Each operation takes logarithmic amortized time.
+    """
+
+    def __init__(self):
+        """
+        Initialize priorityDictionary by creating binary heap of pairs
+        (value,key).  Note that changing or removing a dict entry will not
+        remove the old pair from the heap until it is found by smallest()
+        or until the heap is rebuilt.
+        """
+        self.__heap = []
+        dict.__init__(self)
+
+    def smallest(self):
+        """
+        Find smallest item after removing deleted items from front of heap.
+        """
+        if len(self) == 0:
+            raise IndexError("smallest of empty priorityDictionary")
+        heap = self.__heap
+        while heap[0][1] not in self or self[heap[0][1]] != heap[0][0]:
+            lastItem = heap.pop()
+            insertionPoint = 0
+            while 1:
+                smallChild = 2 * insertionPoint + 1
+                if (
+                    smallChild + 1 < len(heap)
+                    and heap[smallChild] > heap[smallChild + 1]
+                ):
+                    smallChild += 1
+                if smallChild >= len(heap) or lastItem <= heap[smallChild]:
+                    heap[insertionPoint] = lastItem
+                    break
+                heap[insertionPoint] = heap[smallChild]
+                insertionPoint = smallChild
+        return heap[0][1]
+
+    def __iter__(self):
+        """
+        Create destructive sorted iterator of priorityDictionary.
+        """
+
+        def iterfn():
+            while len(self) > 0:
+                x = self.smallest()
+                yield x
+                del self[x]
+
+        return iterfn()
+
+    def __setitem__(self, key, val):
+        """
+        Change value stored in dictionary and add corresponding pair to heap.
+        Rebuilds the heap if the number of deleted items gets large, to avoid
+        memory leakage.
+        """
+        dict.__setitem__(self, key, val)
+        heap = self.__heap
+        if len(heap) > 2 * len(self):
+            self.__heap = [(v, k) for k, v in self.items()]
+            self.__heap.sort()
+        else:
+            newPair = (val, key)
+            insertionPoint = len(heap)
+            heap.append(None)
+            while insertionPoint > 0 and newPair < heap[(insertionPoint - 1) // 2]:
+                heap[insertionPoint] = heap[(insertionPoint - 1) // 2]
+                insertionPoint = (insertionPoint - 1) // 2
+            heap[insertionPoint] = newPair
+
+    def setdefault(self, key, val):
+        """
+        Reimplement setdefault to pass through our customized __setitem__.
+        """
+        if key not in self:
+            self[key] = val
+        return self[key]