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Tim's latest, with some of my changes (also a TP suggestion) added:
instead of testing whether the list changed size after each comparison, temporarily set the type of the list to an immutable list type. This should allow continued use of the list for legitimate purposes but disallows all operations that can change it in any way. (Changes to the internals of list items are not caught, of cause; that's not possible to detect, and it's not necessary to protect the sort code, either.)
This commit is contained in:
Guido van Rossum
parent
32490824b6
commit
4c4e7df755
@ -582,18 +582,15 @@ listappend(self, args)
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/* Comparison function. Takes care of calling a user-supplied
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comparison function (any callable Python object). Calls the
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standard comparison function, cmpobject(), if the user-supplied
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function is NULL. */
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standard comparison function, PyObject_Compare(), if the user-
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supplied function is NULL. */
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static int
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docompare(x, y, compare, list)
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docompare(x, y, compare)
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PyObject *x;
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PyObject *y;
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PyObject *compare;
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PyListObject *list;
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{
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int size = list->ob_size; /* Number of elements to sort */
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PyObject **array = list->ob_item; /* Start of array to sort */
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PyObject *args, *res;
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int i;
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@ -601,11 +598,6 @@ docompare(x, y, compare, list)
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i = PyObject_Compare(x, y);
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if (i && PyErr_Occurred())
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i = CMPERROR;
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else if (size != list->ob_size || array != list->ob_item) {
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PyErr_SetString(PyExc_SystemError,
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"list changed size during sort");
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i = CMPERROR;
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}
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return i;
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}
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@ -622,11 +614,6 @@ docompare(x, y, compare, list)
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"comparison function should return int");
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return CMPERROR;
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}
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if (size != list->ob_size || array != list->ob_item) {
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PyErr_SetString(PyExc_SystemError,
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"list changed size during sort");
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return CMPERROR;
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}
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i = PyInt_AsLong(res);
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Py_DECREF(res);
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if (i < 0)
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@ -636,248 +623,537 @@ docompare(x, y, compare, list)
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return 0;
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}
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/* MINSIZE is the smallest array we care to partition; smaller arrays
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are sorted using binary insertion. It must be at least 4 for the
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quicksort implementation to work. Binary insertion always requires
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fewer compares than quicksort, but does O(N**2) data movement. The
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more expensive compares, the larger MINSIZE should be. */
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#define MINSIZE 49
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/* MINSIZE is the smallest array that will get a full-blown samplesort
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treatment; smaller arrays are sorted using binary insertion. It must
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be at least 7 for the samplesort implementation to work. Binary
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insertion does fewer compares, but can suffer O(N**2) data movement.
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The more expensive compares, the larger MINSIZE should be. */
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#define MINSIZE 100
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/* MINPARTITIONSIZE is the smallest array slice samplesort will bother to
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partition; smaller slices are passed to binarysort. It must be at
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least 2, and no larger than MINSIZE. Setting it higher reduces the #
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of compares slowly, but increases the amount of data movement quickly.
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The value here was chosen assuming a compare costs ~25x more than
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swapping a pair of memory-resident pointers -- but under that assumption,
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changing the value by a few dozen more or less has aggregate effect
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under 1%. So the value is crucial, but not touchy <wink>. */
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#define MINPARTITIONSIZE 40
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/* MAXMERGE is the largest number of elements we'll always merge into
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a known-to-be sorted chunk via binary insertion, regardless of the
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size of that chunk. Given a chunk of N sorted elements, and a group
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of K unknowns, the largest K for which it's better to do insertion
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(than a full-blown sort) is a complicated function of N and K mostly
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involving the expected number of compares and data moves under each
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approach, and the relative cost of those operations on a specific
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architecure. The fixed value here is conservative, and should be a
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clear win regardless of architecture or N. */
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#define MAXMERGE 15
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/* STACKSIZE is the size of our work stack. A rough estimate is that
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this allows us to sort arrays of MINSIZE * 2**STACKSIZE, or large
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enough. (Because of the way we push the biggest partition first,
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the worst case occurs when all subarrays are always partitioned
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exactly in two.) */
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#define STACKSIZE 64
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this allows us to sort arrays of size N where
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N / ln(N) = MINPARTITIONSIZE * 2**STACKSIZE, so 60 is more than enough
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for arrays of size 2**64. Because we push the biggest partition
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first, the worst case occurs when all subarrays are always partitioned
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exactly in two. */
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#define STACKSIZE 60
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/* quicksort algorithm. Return -1 if an exception occurred; in this
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case we leave the array partly sorted but otherwise in good health
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(i.e. no items have been removed or duplicated). */
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#define SETK(X,Y) if ((k = docompare(X,Y,compare))==CMPERROR) goto fail
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/* binarysort is the best method for sorting small arrays: it does
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few compares, but can do data movement quadratic in the number of
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elements.
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[lo, hi) is a contiguous slice of the list, and is sorted via
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binary insertion.
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On entry, must have lo <= start <= hi, and that [lo, start) is already
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sorted (pass start == lo if you don't know!).
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If docompare complains (returns CMPERROR) return -1, else 0.
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Even in case of error, the output slice will be some permutation of
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the input (nothing is lost or duplicated).
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*/
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static int
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quicksort(list, compare)
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PyListObject *list; /* List to sort */
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binarysort(lo, hi, start, list, compare)
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PyObject **lo;
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PyObject **hi;
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PyObject **start;
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PyListObject *list; /* Needed by docompare for paranoia checks */
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PyObject *compare;/* Comparison function object, or NULL for default */
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{
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register PyObject *tmp, *pivot;
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register PyObject **l, **r, **p;
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PyObject **lo, **hi, **notp;
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int top, k, n, lisp, risp;
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PyObject **lostack[STACKSIZE];
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PyObject **histack[STACKSIZE];
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/* Start out with the whole array on the work stack */
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lostack[0] = list->ob_item;
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histack[0] = list->ob_item + list->ob_size;
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top = 1;
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#define SETK(X,Y) if ((k = docompare(X,Y,compare,list))==CMPERROR) goto fail
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/* Repeat until the work stack is empty */
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while (--top >= 0) {
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lo = lostack[top];
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hi = histack[top];
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n = hi - lo;
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/* If it's a small one, use binary insertion sort */
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if (n < MINSIZE) {
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for (notp = lo+1; notp < hi; ++notp) {
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/* set l to where *notp belongs */
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l = lo;
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r = notp;
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pivot = *r;
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do {
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p = l + ((r - l) >> 1);
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SETK(pivot, *p);
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if (k < 0)
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r = p;
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else
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l = p + 1;
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} while (l < r);
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/* Pivot should go at l -- slide over to
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make room. Caution: using memmove
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is much slower under MSVC 5; we're
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not usually moving many slots. */
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for (p = notp; p > l; --p)
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*p = *(p-1);
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*l = pivot;
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}
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continue;
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}
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/* Choose median of first, middle and last as pivot;
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this is a simple unrolled 3-element insertion sort */
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l = lo; /* First */
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p = lo + (n>>1); /* Middle */
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r = hi - 1; /* Last */
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pivot = *p;
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SETK(pivot, *l);
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if (k < 0) {
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*p = *l;
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*l = pivot;
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}
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/* assert lo <= start <= hi
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assert [lo, start) is sorted */
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register int k;
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register PyObject **l, **p, **r;
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register PyObject *pivot;
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if (lo == start)
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++start;
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for (; start < hi; ++start) {
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/* set l to where *start belongs */
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l = lo;
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r = start;
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pivot = *r;
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SETK(pivot, *p);
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if (k < 0) {
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*r = *p;
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*p = pivot; /* for consistency */
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SETK(pivot, *l);
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if (k < 0) {
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*p = *l;
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*l = pivot;
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}
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}
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pivot = *p;
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l++;
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r--;
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/* Partition the array */
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for (;;) {
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lisp = risp = 1; /* presumed guilty */
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/* Move left index to element >= pivot */
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while (l < p) {
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SETK(*l, pivot);
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if (k < 0)
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l++;
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else {
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lisp = 0;
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break;
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}
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}
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/* Move right index to element <= pivot */
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while (r > p) {
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SETK(pivot, *r);
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if (k < 0)
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r--;
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else {
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risp = 0;
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break;
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}
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}
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if (lisp == risp) {
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/* assert l < p < r or l == p == r
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* This is the most common case, so we
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* strive to get back to the top of the
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* loop ASAP.
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*/
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tmp = *l; *l = *r; *r = tmp;
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l++; r--;
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if (l < r)
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continue;
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break;
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}
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/* One (exactly) of the pointers is at p */
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/* assert (p == l) ^ (p == r) */
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notp = lisp ? r : l;
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*p = *notp;
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k = (r - l) >> 1;
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if (k) {
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if (lisp) {
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p = r - k;
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l++;
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}
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else {
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p = l + k;
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r--;
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}
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*notp = *p;
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*p = pivot; /* for consistency */
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continue;
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}
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/* assert l+1 == r */
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*notp = pivot;
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p = notp;
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break;
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} /* end of partitioning loop */
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/* assert *p == pivot
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All in [lo,p) are <= pivot
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At p == pivot
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All in [p+1,hi) are >= pivot
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*/
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r = p;
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l = p + 1;
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/* Partitions are [lo,r) and [l,hi).
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* See whether *l == pivot; we know *l >= pivot, so
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* they're equal iff *l <= pivot too, or not pivot < *l.
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* This wastes a compare if it fails, but can win big
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* when there are runs of duplicates.
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*/
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SETK(pivot, *l);
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if (!(k < 0)) {
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/* Now extend as far as possible (around p) so that:
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All in [lo,r) are <= pivot
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All in [r,l) are == pivot
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All in [l,hi) are >= pivot
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Mildly tricky: continue using only "<" -- we
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deduce equality indirectly.
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*/
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while (r > lo) {
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/* because r-1 < p, *(r-1) <= pivot is known */
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SETK(*(r-1), pivot);
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if (k < 0)
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break;
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/* <= and not < implies == */
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r--;
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}
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l++;
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while (l < hi) {
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/* because l > p, pivot <= *l is known */
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SETK(pivot, *l);
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if (k < 0)
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break;
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/* <= and not < implies == */
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l++;
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}
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} /* end of checking for duplicates */
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/* Push biggest partition first */
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if (r - lo >= hi - l) {
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/* First one is bigger */
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lostack[top] = lo;
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histack[top++] = r;
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lostack[top] = l;
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histack[top++] = hi;
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} else {
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/* Second one is bigger */
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lostack[top] = l;
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histack[top++] = hi;
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lostack[top] = lo;
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histack[top++] = r;
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}
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/* Should assert top <= STACKSIZE */
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do {
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p = l + ((r - l) >> 1);
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SETK(pivot, *p);
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if (k < 0)
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r = p;
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else
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l = p + 1;
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} while (l < r);
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/* Pivot should go at l -- slide over to make room.
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Caution: using memmove is much slower under MSVC 5;
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we're not usually moving many slots. */
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for (p = start; p > l; --p)
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*p = *(p-1);
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*l = pivot;
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}
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/* Success */
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return 0;
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fail:
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return -1;
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}
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/* samplesortslice is the sorting workhorse.
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[lo, hi) is a contiguous slice of the list, to be sorted in place.
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On entry, must have lo <= hi,
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If docompare complains (returns CMPERROR) return -1, else 0.
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Even in case of error, the output slice will be some permutation of
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the input (nothing is lost or duplicated).
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samplesort is basically quicksort on steroids: a power of 2 close
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to n/ln(n) is computed, and that many elements (less 1) are picked at
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random from the array and sorted. These 2**k - 1 elements are then
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used as preselected pivots for an equal number of quicksort
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partitioning steps, partitioning the slice into 2**k chunks each of
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size about ln(n). These small final chunks are then usually handled
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by binarysort. Note that when k=1, this is roughly the same as an
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ordinary quicksort using a random pivot, and when k=2 this is roughly
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a median-of-3 quicksort. From that view, using k ~= lg(n/ln(n)) makes
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this a "median of n/ln(n)" quicksort. You can also view it as a kind
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of bucket sort, where 2**k-1 bucket boundaries are picked dynamically.
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The large number of samples makes a quadratic-time case almost
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impossible, and asymptotically drives the average-case number of
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compares from quicksort's 2 N ln N (or 12/7 N ln N for the median-of-
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3 variant) down to N lg N.
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We also play lots of low-level tricks to cut the number of compares.
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Very obscure: To avoid using extra memory, the PPs are stored in the
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array and shuffled around as partitioning proceeds. At the start of a
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partitioning step, we'll have 2**m-1 (for some m) PPs in sorted order,
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adjacent (either on the left or the right!) to a chunk of X elements
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that are to be partitioned: P X or X P. In either case we need to
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shuffle things *in place* so that the 2**(m-1) smaller PPs are on the
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left, followed by the PP to be used for this step (that's the middle
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of the PPs), followed by X, followed by the 2**(m-1) larger PPs:
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P X or X P -> Psmall pivot X Plarge
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and the order of the PPs must not be altered. It can take a while
|
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to realize this isn't trivial! It can take even longer <wink> to
|
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understand why the simple code below works, using only 2**(m-1) swaps.
|
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The key is that the order of the X elements isn't necessarily
|
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preserved: X can end up as some cyclic permutation of its original
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order. That's OK, because X is unsorted anyway. If the order of X
|
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had to be preserved too, the simplest method I know of using O(1)
|
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scratch storage requires len(X) + 2**(m-1) swaps, spread over 2 passes.
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Since len(X) is typically several times larger than 2**(m-1), that
|
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would slow things down.
|
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*/
|
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|
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struct SamplesortStackNode {
|
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/* Represents a slice of the array, from (& including) lo up
|
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to (but excluding) hi. "extra" additional & adjacent elements
|
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are pre-selected pivots (PPs), spanning [lo-extra, lo) if
|
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extra > 0, or [hi, hi-extra) if extra < 0. The PPs are
|
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already sorted, but nothing is known about the other elements
|
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in [lo, hi). |extra| is always one less than a power of 2.
|
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When extra is 0, we're out of PPs, and the slice must be
|
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sorted by some other means. */
|
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PyObject **lo;
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PyObject **hi;
|
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int extra;
|
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};
|
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|
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/* The number of PPs we want is 2**k - 1, where 2**k is as close to
|
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N / ln(N) as possible. So k ~= lg(N / ln(N). Calling libm routines
|
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is undesirable, so cutoff values are canned in the "cutoff" table
|
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below: cutoff[i] is the smallest N such that k == CUTOFFBASE + i. */
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#define CUTOFFBASE 4
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static int cutoff[] = {
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43, /* smallest N such that k == 4 */
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106, /* etc */
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250,
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576,
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1298,
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2885,
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6339,
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13805,
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29843,
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64116,
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137030,
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291554,
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617916,
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1305130,
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2748295,
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5771662,
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12091672,
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25276798,
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52734615,
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109820537,
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228324027,
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473977813,
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982548444, /* smallest N such that k == 26 */
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2034159050 /* largest N that fits in signed 32-bit; k == 27 */
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};
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|
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static int
|
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samplesortslice(lo, hi, list, compare)
|
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PyObject **lo;
|
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PyObject **hi;
|
||||
PyListObject *list; /* Needed by docompare for paranoia checks */
|
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PyObject *compare;/* Comparison function object, or NULL for default */
|
||||
{
|
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register PyObject **l, **r;
|
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register PyObject *tmp, *pivot;
|
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register int k;
|
||||
int n, extra, top, extraOnRight;
|
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struct SamplesortStackNode stack[STACKSIZE];
|
||||
|
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/* assert lo <= hi */
|
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n = hi - lo;
|
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|
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/* ----------------------------------------------------------
|
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* Special cases
|
||||
* --------------------------------------------------------*/
|
||||
if (n < 2)
|
||||
return 0;
|
||||
|
||||
/* Set r to the largest value such that [lo,r) is sorted.
|
||||
This catches the already-sorted case, the all-the-same
|
||||
case, and the appended-a-few-elements-to-a-sorted-list case.
|
||||
If the array is unsorted, we're very likely to get out of
|
||||
the loop fast, so the test is cheap if it doesn't pay off.
|
||||
*/
|
||||
/* assert lo < hi */
|
||||
for (r = lo+1; r < hi; ++r) {
|
||||
SETK(*r, *(r-1));
|
||||
if (k < 0)
|
||||
break;
|
||||
}
|
||||
/* [lo,r) is sorted, [r,hi) unknown. Get out cheap if there are
|
||||
few unknowns, or few elements in total. */
|
||||
if (hi - r <= MAXMERGE || n < MINSIZE)
|
||||
return binarysort(lo, hi, r, list, compare);
|
||||
|
||||
/* Check for the array already being reverse-sorted. Typical
|
||||
benchmark-driven silliness <wink>. */
|
||||
/* assert lo < hi */
|
||||
for (r = lo+1; r < hi; ++r) {
|
||||
SETK(*(r-1), *r);
|
||||
if (k < 0)
|
||||
break;
|
||||
}
|
||||
if (hi - r <= MAXMERGE) {
|
||||
/* Reverse the reversed prefix, then insert the tail */
|
||||
PyObject **originalr = r;
|
||||
l = lo;
|
||||
do {
|
||||
--r;
|
||||
tmp = *l; *l = *r; *r = tmp;
|
||||
++l;
|
||||
} while (l < r);
|
||||
return binarysort(lo, hi, originalr, list, compare);
|
||||
}
|
||||
|
||||
/* ----------------------------------------------------------
|
||||
* Normal case setup: a large array without obvious pattern.
|
||||
* --------------------------------------------------------*/
|
||||
|
||||
/* extra := a power of 2 ~= n/ln(n), less 1.
|
||||
First find the smallest extra s.t. n < cutoff[extra] */
|
||||
for (extra = 0;
|
||||
extra < sizeof(cutoff) / sizeof(cutoff[0]);
|
||||
++extra) {
|
||||
if (n < cutoff[extra])
|
||||
break;
|
||||
/* note that if we fall out of the loop, the value of
|
||||
extra still makes *sense*, but may be smaller than
|
||||
we would like (but the array has more than ~= 2**31
|
||||
elements in this case!) */
|
||||
}
|
||||
/* Now k == extra - 1 + CUTOFFBASE. The smallest value k can
|
||||
have is CUTOFFBASE-1, so
|
||||
assert MINSIZE >= 2**(CUTOFFBASE-1) - 1 */
|
||||
extra = (1 << (extra - 1 + CUTOFFBASE)) - 1;
|
||||
/* assert extra > 0 and n >= extra */
|
||||
|
||||
/* Swap that many values to the start of the array. The
|
||||
selection of elements is pseudo-random, but the same on
|
||||
every run (this is intentional! timing algorithm changes is
|
||||
a pain if timing varies across runs). */
|
||||
{
|
||||
unsigned int seed = n / extra; /* arbitrary */
|
||||
unsigned int i;
|
||||
for (i = 0; i < (unsigned)extra; ++i) {
|
||||
/* j := random int in [i, n) */
|
||||
unsigned int j;
|
||||
seed = seed * 69069 + 7;
|
||||
j = i + seed % (n - i);
|
||||
tmp = lo[i]; lo[i] = lo[j]; lo[j] = tmp;
|
||||
}
|
||||
}
|
||||
|
||||
/* Recursively sort the preselected pivots. */
|
||||
if (samplesortslice(lo, lo + extra, list, compare) < 0)
|
||||
goto fail;
|
||||
|
||||
top = 0; /* index of available stack slot */
|
||||
lo += extra; /* point to first unknown */
|
||||
extraOnRight = 0; /* the PPs are at the left end */
|
||||
|
||||
/* ----------------------------------------------------------
|
||||
* Partition [lo, hi), and repeat until out of work.
|
||||
* --------------------------------------------------------*/
|
||||
for (;;) {
|
||||
/* assert lo <= hi, so n >= 0 */
|
||||
n = hi - lo;
|
||||
|
||||
/* We may not want, or may not be able, to partition:
|
||||
If n is small, it's quicker to insert.
|
||||
If extra is 0, we're out of pivots, and *must* use
|
||||
another method.
|
||||
*/
|
||||
if (n < MINPARTITIONSIZE || extra == 0) {
|
||||
if (n >= MINSIZE) {
|
||||
/* assert extra == 0
|
||||
This is rare, since the average size
|
||||
of a final block is only about
|
||||
ln(original n). */
|
||||
if (samplesortslice(lo, hi, list,
|
||||
compare) < 0)
|
||||
goto fail;
|
||||
}
|
||||
else {
|
||||
/* Binary insertion should be quicker,
|
||||
and we can take advantage of the PPs
|
||||
already being sorted. */
|
||||
if (extraOnRight && extra) {
|
||||
/* swap the PPs to the left end */
|
||||
k = extra;
|
||||
do {
|
||||
tmp = *lo;
|
||||
*lo = *hi;
|
||||
*hi = tmp;
|
||||
++lo; ++hi;
|
||||
} while (--k);
|
||||
}
|
||||
if (binarysort(lo - extra, hi, lo,
|
||||
list, compare) < 0)
|
||||
goto fail;
|
||||
}
|
||||
|
||||
/* Find another slice to work on. */
|
||||
if (--top < 0)
|
||||
break; /* no more -- done! */
|
||||
lo = stack[top].lo;
|
||||
hi = stack[top].hi;
|
||||
extra = stack[top].extra;
|
||||
extraOnRight = 0;
|
||||
if (extra < 0) {
|
||||
extraOnRight = 1;
|
||||
extra = -extra;
|
||||
}
|
||||
continue;
|
||||
}
|
||||
|
||||
/* Pretend the PPs are indexed 0, 1, ..., extra-1.
|
||||
Then our preselected pivot is at (extra-1)/2, and we
|
||||
want to move the PPs before that to the left end of
|
||||
the slice, and the PPs after that to the right end.
|
||||
The following section changes extra, lo, hi, and the
|
||||
slice such that:
|
||||
[lo-extra, lo) contains the smaller PPs.
|
||||
*lo == our PP.
|
||||
(lo, hi) contains the unknown elements.
|
||||
[hi, hi+extra) contains the larger PPs.
|
||||
*/
|
||||
k = extra >>= 1; /* num PPs to move */
|
||||
if (extraOnRight) {
|
||||
/* Swap the smaller PPs to the left end.
|
||||
Note that this loop actually moves k+1 items:
|
||||
the last is our PP */
|
||||
do {
|
||||
tmp = *lo; *lo = *hi; *hi = tmp;
|
||||
++lo; ++hi;
|
||||
} while (k--);
|
||||
}
|
||||
else {
|
||||
/* Swap the larger PPs to the right end. */
|
||||
while (k--) {
|
||||
--lo; --hi;
|
||||
tmp = *lo; *lo = *hi; *hi = tmp;
|
||||
}
|
||||
}
|
||||
--lo; /* *lo is now our PP */
|
||||
pivot = *lo;
|
||||
|
||||
/* Now an almost-ordinary quicksort partition step.
|
||||
Note that most of the time is spent here!
|
||||
Only odd thing is that we partition into < and >=,
|
||||
instead of the usual <= and >=. This helps when
|
||||
there are lots of duplicates of different values,
|
||||
because it eventually tends to make subfiles
|
||||
"pure" (all duplicates), and we special-case for
|
||||
duplicates later. */
|
||||
l = lo + 1;
|
||||
r = hi - 1;
|
||||
/* assert lo < l < r < hi (small n weeded out above) */
|
||||
|
||||
do {
|
||||
/* slide l right, looking for key >= pivot */
|
||||
do {
|
||||
SETK(*l, pivot);
|
||||
if (k < 0)
|
||||
++l;
|
||||
else
|
||||
break;
|
||||
} while (l < r);
|
||||
|
||||
/* slide r left, looking for key < pivot */
|
||||
while (l < r) {
|
||||
SETK(*r, pivot);
|
||||
if (k < 0)
|
||||
break;
|
||||
else
|
||||
--r;
|
||||
}
|
||||
|
||||
/* swap and advance both pointers */
|
||||
if (l < r) {
|
||||
tmp = *l; *l = *r; *r = tmp;
|
||||
++l;
|
||||
--r;
|
||||
}
|
||||
|
||||
} while (l < r);
|
||||
|
||||
/* assert lo < r <= l < hi
|
||||
assert r == l or r+1 == l
|
||||
everything to the left of l is < pivot, and
|
||||
everything to the right of r is >= pivot */
|
||||
|
||||
if (l == r) {
|
||||
SETK(*r, pivot);
|
||||
if (k < 0)
|
||||
++l;
|
||||
else
|
||||
--r;
|
||||
}
|
||||
/* assert lo <= r and r+1 == l and l <= hi
|
||||
assert r == lo or a[r] < pivot
|
||||
assert a[lo] is pivot
|
||||
assert l == hi or a[l] >= pivot
|
||||
Swap the pivot into "the middle", so we can henceforth
|
||||
ignore it.
|
||||
*/
|
||||
*lo = *r;
|
||||
*r = pivot;
|
||||
|
||||
/* The following is true now, & will be preserved:
|
||||
All in [lo,r) are < pivot
|
||||
All in [r,l) == pivot (& so can be ignored)
|
||||
All in [l,hi) are >= pivot */
|
||||
|
||||
/* Check for duplicates of the pivot. One compare is
|
||||
wasted if there are no duplicates, but can win big
|
||||
when there are.
|
||||
Tricky: we're sticking to "<" compares, so deduce
|
||||
equality indirectly. We know pivot <= *l, so they're
|
||||
equal iff not pivot < *l.
|
||||
*/
|
||||
while (l < hi) {
|
||||
/* pivot <= *l known */
|
||||
SETK(pivot, *l);
|
||||
if (k < 0)
|
||||
break;
|
||||
else
|
||||
/* <= and not < implies == */
|
||||
++l;
|
||||
}
|
||||
|
||||
/* assert lo <= r < l <= hi
|
||||
Partitions are [lo, r) and [l, hi) */
|
||||
|
||||
/* push fattest first; remember we still have extra PPs
|
||||
to the left of the left chunk and to the right of
|
||||
the right chunk! */
|
||||
/* assert top < STACKSIZE */
|
||||
if (r - lo <= hi - l) {
|
||||
/* second is bigger */
|
||||
stack[top].lo = l;
|
||||
stack[top].hi = hi;
|
||||
stack[top].extra = -extra;
|
||||
hi = r;
|
||||
extraOnRight = 0;
|
||||
}
|
||||
else {
|
||||
/* first is bigger */
|
||||
stack[top].lo = lo;
|
||||
stack[top].hi = r;
|
||||
stack[top].extra = extra;
|
||||
lo = l;
|
||||
extraOnRight = 1;
|
||||
}
|
||||
++top;
|
||||
|
||||
} /* end of partitioning loop */
|
||||
|
||||
return 0;
|
||||
|
||||
fail:
|
||||
return -1;
|
||||
}
|
||||
|
||||
#undef SETK
|
||||
}
|
||||
|
||||
staticforward PyTypeObject immutable_list_type;
|
||||
|
||||
static PyObject *
|
||||
listsort(self, compare)
|
||||
PyListObject *self;
|
||||
PyObject *compare;
|
||||
{
|
||||
if (quicksort(self, compare) < 0)
|
||||
int err;
|
||||
|
||||
self->ob_type = &immutable_list_type;
|
||||
err = samplesortslice(self->ob_item,
|
||||
self->ob_item + self->ob_size,
|
||||
self, compare);
|
||||
self->ob_type = &PyList_Type;
|
||||
if (err < 0)
|
||||
return NULL;
|
||||
Py_INCREF(Py_None);
|
||||
return Py_None;
|
||||
}
|
||||
|
||||
int
|
||||
PyList_Sort(v)
|
||||
PyObject *v;
|
||||
{
|
||||
if (v == NULL || !PyList_Check(v)) {
|
||||
PyErr_BadInternalCall();
|
||||
return -1;
|
||||
}
|
||||
v = listsort((PyListObject *)v, (PyObject *)NULL);
|
||||
if (v == NULL)
|
||||
return -1;
|
||||
Py_DECREF(v);
|
||||
return 0;
|
||||
}
|
||||
|
||||
static PyObject *
|
||||
listreverse(self, args)
|
||||
PyListObject *self;
|
||||
@ -919,17 +1195,6 @@ PyList_Reverse(v)
|
||||
return 0;
|
||||
}
|
||||
|
||||
int
|
||||
PyList_Sort(v)
|
||||
PyObject *v;
|
||||
{
|
||||
if (v == NULL || !PyList_Check(v)) {
|
||||
PyErr_BadInternalCall();
|
||||
return -1;
|
||||
}
|
||||
return quicksort((PyListObject *)v, (PyObject *)NULL);
|
||||
}
|
||||
|
||||
PyObject *
|
||||
PyList_AsTuple(v)
|
||||
PyObject *v;
|
||||
@ -1069,3 +1334,71 @@ PyTypeObject PyList_Type = {
|
||||
&list_as_sequence, /*tp_as_sequence*/
|
||||
0, /*tp_as_mapping*/
|
||||
};
|
||||
|
||||
|
||||
/* During a sort, we really can't have anyone modifying the list; it could
|
||||
cause core dumps. Thus, we substitute a dummy type that raises an
|
||||
explanatory exception when a modifying operation is used. Caveat:
|
||||
comparisons may behave differently; but I guess it's a bad idea anyway to
|
||||
compare a list that's being sorted... */
|
||||
|
||||
static PyObject *
|
||||
immutable_list_op(/*No args!*/)
|
||||
{
|
||||
PyErr_SetString(PyExc_TypeError,
|
||||
"a list cannot be modified while it is being sorted");
|
||||
return NULL;
|
||||
}
|
||||
|
||||
static PyMethodDef immutable_list_methods[] = {
|
||||
{"append", (PyCFunction)immutable_list_op},
|
||||
{"insert", (PyCFunction)immutable_list_op},
|
||||
{"remove", (PyCFunction)immutable_list_op},
|
||||
{"index", (PyCFunction)listindex},
|
||||
{"count", (PyCFunction)listcount},
|
||||
{"reverse", (PyCFunction)immutable_list_op},
|
||||
{"sort", (PyCFunction)immutable_list_op},
|
||||
{NULL, NULL} /* sentinel */
|
||||
};
|
||||
|
||||
static PyObject *
|
||||
immutable_list_getattr(f, name)
|
||||
PyListObject *f;
|
||||
char *name;
|
||||
{
|
||||
return Py_FindMethod(immutable_list_methods, (PyObject *)f, name);
|
||||
}
|
||||
|
||||
static int
|
||||
immutable_list_ass(/*No args!*/)
|
||||
{
|
||||
immutable_list_op();
|
||||
return -1;
|
||||
}
|
||||
|
||||
static PySequenceMethods immutable_list_as_sequence = {
|
||||
(inquiry)list_length, /*sq_length*/
|
||||
(binaryfunc)list_concat, /*sq_concat*/
|
||||
(intargfunc)list_repeat, /*sq_repeat*/
|
||||
(intargfunc)list_item, /*sq_item*/
|
||||
(intintargfunc)list_slice, /*sq_slice*/
|
||||
(intobjargproc)immutable_list_ass, /*sq_ass_item*/
|
||||
(intintobjargproc)immutable_list_ass, /*sq_ass_slice*/
|
||||
};
|
||||
|
||||
static PyTypeObject immutable_list_type = {
|
||||
PyObject_HEAD_INIT(&PyType_Type)
|
||||
0,
|
||||
"list (immutable, during sort)",
|
||||
sizeof(PyListObject),
|
||||
0,
|
||||
0, /*tp_dealloc*/ /* Cannot happen */
|
||||
(printfunc)list_print, /*tp_print*/
|
||||
(getattrfunc)immutable_list_getattr, /*tp_getattr*/
|
||||
0, /*tp_setattr*/
|
||||
0, /*tp_compare*/ /* Won't be called */
|
||||
(reprfunc)list_repr, /*tp_repr*/
|
||||
0, /*tp_as_number*/
|
||||
&immutable_list_as_sequence, /*tp_as_sequence*/
|
||||
0, /*tp_as_mapping*/
|
||||
};
|
||||
|
||||
Loading…
Reference in New Issue
Block a user