+++ /dev/null
-/* Drop in replacement for heapq.py
-
-C implementation derived directly from heapq.py in Py2.3
-which was written by Kevin O'Connor, augmented by Tim Peters,
-annotated by François Pinard, and converted to C by Raymond Hettinger.
-
-*/
-
-#include "Python.h"
-
-static int
-_siftdown(PyListObject *heap, int startpos, int pos)
-{
- PyObject *newitem, *parent;
- int cmp, parentpos;
-
- assert(PyList_Check(heap));
- if (pos >= PyList_GET_SIZE(heap)) {
- PyErr_SetString(PyExc_IndexError, "index out of range");
- return -1;
- }
-
- newitem = PyList_GET_ITEM(heap, pos);
- Py_INCREF(newitem);
- /* Follow the path to the root, moving parents down until finding
- a place newitem fits. */
- while (pos > startpos){
- parentpos = (pos - 1) >> 1;
- parent = PyList_GET_ITEM(heap, parentpos);
- cmp = PyObject_RichCompareBool(parent, newitem, Py_LE);
- if (cmp == -1)
- return -1;
- if (cmp == 1)
- break;
- Py_INCREF(parent);
- Py_DECREF(PyList_GET_ITEM(heap, pos));
- PyList_SET_ITEM(heap, pos, parent);
- pos = parentpos;
- }
- Py_DECREF(PyList_GET_ITEM(heap, pos));
- PyList_SET_ITEM(heap, pos, newitem);
- return 0;
-}
-
-static int
-_siftup(PyListObject *heap, int pos)
-{
- int startpos, endpos, childpos, rightpos;
- int cmp;
- PyObject *newitem, *tmp;
-
- assert(PyList_Check(heap));
- endpos = PyList_GET_SIZE(heap);
- startpos = pos;
- if (pos >= endpos) {
- PyErr_SetString(PyExc_IndexError, "index out of range");
- return -1;
- }
- newitem = PyList_GET_ITEM(heap, pos);
- Py_INCREF(newitem);
-
- /* Bubble up the smaller child until hitting a leaf. */
- childpos = 2*pos + 1; /* leftmost child position */
- while (childpos < endpos) {
- /* Set childpos to index of smaller child. */
- rightpos = childpos + 1;
- if (rightpos < endpos) {
- cmp = PyObject_RichCompareBool(
- PyList_GET_ITEM(heap, rightpos),
- PyList_GET_ITEM(heap, childpos),
- Py_LE);
- if (cmp == -1)
- return -1;
- if (cmp == 1)
- childpos = rightpos;
- }
- /* Move the smaller child up. */
- tmp = PyList_GET_ITEM(heap, childpos);
- Py_INCREF(tmp);
- Py_DECREF(PyList_GET_ITEM(heap, pos));
- PyList_SET_ITEM(heap, pos, tmp);
- pos = childpos;
- childpos = 2*pos + 1;
- }
-
- /* The leaf at pos is empty now. Put newitem there, and and bubble
- it up to its final resting place (by sifting its parents down). */
- Py_DECREF(PyList_GET_ITEM(heap, pos));
- PyList_SET_ITEM(heap, pos, newitem);
- return _siftdown(heap, startpos, pos);
-}
-
-static PyObject *
-heappush(PyObject *self, PyObject *args)
-{
- PyObject *heap, *item;
-
- if (!PyArg_UnpackTuple(args, "heappush", 2, 2, &heap, &item))
- return NULL;
-
- if (!PyList_Check(heap)) {
- PyErr_SetString(PyExc_TypeError, "heap argument must be a list");
- return NULL;
- }
-
- if (PyList_Append(heap, item) == -1)
- return NULL;
-
- if (_siftdown((PyListObject *)heap, 0, PyList_GET_SIZE(heap)-1) == -1)
- return NULL;
- Py_INCREF(Py_None);
- return Py_None;
-}
-
-PyDoc_STRVAR(heappush_doc,
-"Push item onto heap, maintaining the heap invariant.");
-
-static PyObject *
-heappop(PyObject *self, PyObject *heap)
-{
- PyObject *lastelt, *returnitem;
- int n;
-
- if (!PyList_Check(heap)) {
- PyErr_SetString(PyExc_TypeError, "heap argument must be a list");
- return NULL;
- }
-
- /* # raises appropriate IndexError if heap is empty */
- n = PyList_GET_SIZE(heap);
- if (n == 0) {
- PyErr_SetString(PyExc_IndexError, "index out of range");
- return NULL;
- }
-
- lastelt = PyList_GET_ITEM(heap, n-1) ;
- Py_INCREF(lastelt);
- PyList_SetSlice(heap, n-1, n, NULL);
- n--;
-
- if (!n)
- return lastelt;
- returnitem = PyList_GET_ITEM(heap, 0);
- PyList_SET_ITEM(heap, 0, lastelt);
- if (_siftup((PyListObject *)heap, 0) == -1) {
- Py_DECREF(returnitem);
- return NULL;
- }
- return returnitem;
-}
-
-PyDoc_STRVAR(heappop_doc,
-"Pop the smallest item off the heap, maintaining the heap invariant.");
-
-static PyObject *
-heapreplace(PyObject *self, PyObject *args)
-{
- PyObject *heap, *item, *returnitem;
-
- if (!PyArg_UnpackTuple(args, "heapreplace", 2, 2, &heap, &item))
- return NULL;
-
- if (!PyList_Check(heap)) {
- PyErr_SetString(PyExc_TypeError, "heap argument must be a list");
- return NULL;
- }
-
- if (PyList_GET_SIZE(heap) < 1) {
- PyErr_SetString(PyExc_IndexError, "index out of range");
- return NULL;
- }
-
- returnitem = PyList_GET_ITEM(heap, 0);
- Py_INCREF(item);
- PyList_SET_ITEM(heap, 0, item);
- if (_siftup((PyListObject *)heap, 0) == -1) {
- Py_DECREF(returnitem);
- return NULL;
- }
- return returnitem;
-}
-
-PyDoc_STRVAR(heapreplace_doc,
-"Pop and return the current smallest value, and add the new item.\n\
-\n\
-This is more efficient than heappop() followed by heappush(), and can be\n\
-more appropriate when using a fixed-size heap. Note that the value\n\
-returned may be larger than item! That constrains reasonable uses of\n\
-this routine.\n");
-
-static PyObject *
-heapify(PyObject *self, PyObject *heap)
-{
- int i, n;
-
- if (!PyList_Check(heap)) {
- PyErr_SetString(PyExc_TypeError, "heap argument must be a list");
- return NULL;
- }
-
- n = PyList_GET_SIZE(heap);
- /* Transform bottom-up. The largest index there's any point to
- looking at is the largest with a child index in-range, so must
- have 2*i + 1 < n, or i < (n-1)/2. If n is even = 2*j, this is
- (2*j-1)/2 = j-1/2 so j-1 is the largest, which is n//2 - 1. If
- n is odd = 2*j+1, this is (2*j+1-1)/2 = j so j-1 is the largest,
- and that's again n//2-1.
- */
- for (i=n/2-1 ; i>=0 ; i--)
- if(_siftup((PyListObject *)heap, i) == -1)
- return NULL;
- Py_INCREF(Py_None);
- return Py_None;
-}
-
-PyDoc_STRVAR(heapify_doc,
-"Transform list into a heap, in-place, in O(len(heap)) time.");
-
-static PyMethodDef heapq_methods[] = {
- {"heappush", (PyCFunction)heappush,
- METH_VARARGS, heappush_doc},
- {"heappop", (PyCFunction)heappop,
- METH_O, heappop_doc},
- {"heapreplace", (PyCFunction)heapreplace,
- METH_VARARGS, heapreplace_doc},
- {"heapify", (PyCFunction)heapify,
- METH_O, heapify_doc},
- {NULL, NULL} /* sentinel */
-};
-
-PyDoc_STRVAR(module_doc,
-"Heap queue algorithm (a.k.a. priority queue).\n\
-\n\
-Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for\n\
-all k, counting elements from 0. For the sake of comparison,\n\
-non-existing elements are considered to be infinite. The interesting\n\
-property of a heap is that a[0] is always its smallest element.\n\
-\n\
-Usage:\n\
-\n\
-heap = [] # creates an empty heap\n\
-heappush(heap, item) # pushes a new item on the heap\n\
-item = heappop(heap) # pops the smallest item from the heap\n\
-item = heap[0] # smallest item on the heap without popping it\n\
-heapify(x) # transforms list into a heap, in-place, in linear time\n\
-item = heapreplace(heap, item) # pops and returns smallest item, and adds\n\
- # new item; the heap size is unchanged\n\
-\n\
-Our API differs from textbook heap algorithms as follows:\n\
-\n\
-- We use 0-based indexing. This makes the relationship between the\n\
- index for a node and the indexes for its children slightly less\n\
- obvious, but is more suitable since Python uses 0-based indexing.\n\
-\n\
-- Our heappop() method returns the smallest item, not the largest.\n\
-\n\
-These two make it possible to view the heap as a regular Python list\n\
-without surprises: heap[0] is the smallest item, and heap.sort()\n\
-maintains the heap invariant!\n");
-
-
-PyDoc_STRVAR(__about__,
-"Heap queues\n\
-\n\
-[explanation by François Pinard]\n\
-\n\
-Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for\n\
-all k, counting elements from 0. For the sake of comparison,\n\
-non-existing elements are considered to be infinite. The interesting\n\
-property of a heap is that a[0] is always its smallest element.\n"
-"\n\
-The strange invariant above is meant to be an efficient memory\n\
-representation for a tournament. The numbers below are `k', not a[k]:\n\
-\n\
- 0\n\
-\n\
- 1 2\n\
-\n\
- 3 4 5 6\n\
-\n\
- 7 8 9 10 11 12 13 14\n\
-\n\
- 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30\n\
-\n\
-\n\
-In the tree above, each cell `k' is topping `2*k+1' and `2*k+2'. In\n\
-an usual binary tournament we see in sports, each cell is the winner\n\
-over the two cells it tops, and we can trace the winner down the tree\n\
-to see all opponents s/he had. However, in many computer applications\n\
-of such tournaments, we do not need to trace the history of a winner.\n\
-To be more memory efficient, when a winner is promoted, we try to\n\
-replace it by something else at a lower level, and the rule becomes\n\
-that a cell and the two cells it tops contain three different items,\n\
-but the top cell \"wins\" over the two topped cells.\n"
-"\n\
-If this heap invariant is protected at all time, index 0 is clearly\n\
-the overall winner. The simplest algorithmic way to remove it and\n\
-find the \"next\" winner is to move some loser (let's say cell 30 in the\n\
-diagram above) into the 0 position, and then percolate this new 0 down\n\
-the tree, exchanging values, until the invariant is re-established.\n\
-This is clearly logarithmic on the total number of items in the tree.\n\
-By iterating over all items, you get an O(n ln n) sort.\n"
-"\n\
-A nice feature of this sort is that you can efficiently insert new\n\
-items while the sort is going on, provided that the inserted items are\n\
-not \"better\" than the last 0'th element you extracted. This is\n\
-especially useful in simulation contexts, where the tree holds all\n\
-incoming events, and the \"win\" condition means the smallest scheduled\n\
-time. When an event schedule other events for execution, they are\n\
-scheduled into the future, so they can easily go into the heap. So, a\n\
-heap is a good structure for implementing schedulers (this is what I\n\
-used for my MIDI sequencer :-).\n"
-"\n\
-Various structures for implementing schedulers have been extensively\n\
-studied, and heaps are good for this, as they are reasonably speedy,\n\
-the speed is almost constant, and the worst case is not much different\n\
-than the average case. However, there are other representations which\n\
-are more efficient overall, yet the worst cases might be terrible.\n"
-"\n\
-Heaps are also very useful in big disk sorts. You most probably all\n\
-know that a big sort implies producing \"runs\" (which are pre-sorted\n\
-sequences, which size is usually related to the amount of CPU memory),\n\
-followed by a merging passes for these runs, which merging is often\n\
-very cleverly organised[1]. It is very important that the initial\n\
-sort produces the longest runs possible. Tournaments are a good way\n\
-to that. If, using all the memory available to hold a tournament, you\n\
-replace and percolate items that happen to fit the current run, you'll\n\
-produce runs which are twice the size of the memory for random input,\n\
-and much better for input fuzzily ordered.\n"
-"\n\
-Moreover, if you output the 0'th item on disk and get an input which\n\
-may not fit in the current tournament (because the value \"wins\" over\n\
-the last output value), it cannot fit in the heap, so the size of the\n\
-heap decreases. The freed memory could be cleverly reused immediately\n\
-for progressively building a second heap, which grows at exactly the\n\
-same rate the first heap is melting. When the first heap completely\n\
-vanishes, you switch heaps and start a new run. Clever and quite\n\
-effective!\n\
-\n\
-In a word, heaps are useful memory structures to know. I use them in\n\
-a few applications, and I think it is good to keep a `heap' module\n\
-around. :-)\n"
-"\n\
---------------------\n\
-[1] The disk balancing algorithms which are current, nowadays, are\n\
-more annoying than clever, and this is a consequence of the seeking\n\
-capabilities of the disks. On devices which cannot seek, like big\n\
-tape drives, the story was quite different, and one had to be very\n\
-clever to ensure (far in advance) that each tape movement will be the\n\
-most effective possible (that is, will best participate at\n\
-\"progressing\" the merge). Some tapes were even able to read\n\
-backwards, and this was also used to avoid the rewinding time.\n\
-Believe me, real good tape sorts were quite spectacular to watch!\n\
-From all times, sorting has always been a Great Art! :-)\n");
-
-PyMODINIT_FUNC
-initheapq(void)
-{
- PyObject *m;
-
- m = Py_InitModule3("heapq", heapq_methods, module_doc);
- PyModule_AddObject(m, "__about__", PyString_FromString(__about__));
-}
-
projects / python / commitdiff