* Restore the pure python version of heapq.py.

* Mark the C version as private and only use when available.

1 files changed, 0 insertions, 364 deletions

diff --git a/Modules/heapqmodule.c b/Modules/heapqmodule.c
deleted file mode 100644
index 6bcc71d..0000000
--- a/Modules/heapqmodule.c
+++ /dev/null
@@ -1,364 +0,0 @@
-/* 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__));
-}
-