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An RBTree is a fast, balanced efficient data structure with the following properties: get O(log n) set O(log n) delete O(log n) min O(log n) max O(log n) contains O(log n) Because the worst case timing is minimal across the range of standard dict and ordered data operations it makes sense to use this when you have volatile/dynamic sorted data. In common usage its nearly as fast as the Python dict impl but has a slightly more expensive usage of the compare function as the keys are ordered and not hashed. WWW: http://www.python.org/pypi/RBTree/
37 lines
1,003 B
C
37 lines
1,003 B
C
--- ./src/rbtree_impl.c.orig Mon May 8 11:00:16 2006
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+++ ./src/rbtree_impl.c Mon May 8 11:01:48 2006
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@@ -43,10 +43,11 @@
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int
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rbtree_node_compare(PyObject *x, PyObject *y)
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{
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+ int gt, lt;
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/* a three way compare that should work with whatever objects support */
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- int gt = PyObject_RichCompareBool(x,y, Py_GT);
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+ gt = PyObject_RichCompareBool(x,y, Py_GT);
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if (gt == 1) return 1;
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- int lt = PyObject_RichCompareBool(x,y, Py_LT);
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+ lt = PyObject_RichCompareBool(x,y, Py_LT);
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if (lt == 1) return -1;
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return 0;
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}
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@@ -626,16 +627,18 @@
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rbtree_node_t *
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tree_min(rbtree_t *T, rbtree_node_t *x) {
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+ rbtree_node_t *n;
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if (x == NULL) x = T->root;
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- rbtree_node_t *n = __tree_min(T, x);
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+ n = __tree_min(T, x);
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if (n == T->nil) n = NULL;
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return n;
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}
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rbtree_node_t *
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tree_max(rbtree_t *T, rbtree_node_t *x) {
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+ rbtree_node_t *n;
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if (x == NULL) x = T->root;
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- rbtree_node_t *n = __tree_max(T, x);
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+ n = __tree_max(T, x);
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if (n == T->nil) n = NULL;
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return n;
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}
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