mirror of https://github.com/python/cpython
* Restore the pure python version of heapq.py.
* Mark the C version as private and only use when available.
This commit is contained in:
Raymond Hettinger
parent
c46cb2a1a9
commit
1660e0c1f1
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/* Drop in replacement for heapq.py
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C implementation derived directly from heapq.py in Py2.3
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which was written by Kevin O'Connor, augmented by Tim Peters,
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annotated by François Pinard, and converted to C by Raymond Hettinger.
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*/
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#include "Python.h"
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static int
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_siftdown(PyListObject *heap, int startpos, int pos)
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{
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PyObject *newitem, *parent;
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int cmp, parentpos;
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assert(PyList_Check(heap));
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if (pos >= PyList_GET_SIZE(heap)) {
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PyErr_SetString(PyExc_IndexError, "index out of range");
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return -1;
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}
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newitem = PyList_GET_ITEM(heap, pos);
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Py_INCREF(newitem);
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/* Follow the path to the root, moving parents down until finding
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a place newitem fits. */
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while (pos > startpos){
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parentpos = (pos - 1) >> 1;
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parent = PyList_GET_ITEM(heap, parentpos);
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cmp = PyObject_RichCompareBool(parent, newitem, Py_LE);
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if (cmp == -1)
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return -1;
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if (cmp == 1)
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break;
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Py_INCREF(parent);
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Py_DECREF(PyList_GET_ITEM(heap, pos));
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PyList_SET_ITEM(heap, pos, parent);
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pos = parentpos;
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}
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Py_DECREF(PyList_GET_ITEM(heap, pos));
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PyList_SET_ITEM(heap, pos, newitem);
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return 0;
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}
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static int
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_siftup(PyListObject *heap, int pos)
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{
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int startpos, endpos, childpos, rightpos;
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int cmp;
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PyObject *newitem, *tmp;
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assert(PyList_Check(heap));
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endpos = PyList_GET_SIZE(heap);
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startpos = pos;
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if (pos >= endpos) {
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PyErr_SetString(PyExc_IndexError, "index out of range");
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return -1;
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}
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newitem = PyList_GET_ITEM(heap, pos);
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Py_INCREF(newitem);
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/* Bubble up the smaller child until hitting a leaf. */
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childpos = 2*pos + 1; /* leftmost child position */
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while (childpos < endpos) {
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/* Set childpos to index of smaller child. */
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rightpos = childpos + 1;
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if (rightpos < endpos) {
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cmp = PyObject_RichCompareBool(
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PyList_GET_ITEM(heap, rightpos),
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PyList_GET_ITEM(heap, childpos),
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Py_LE);
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if (cmp == -1)
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return -1;
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if (cmp == 1)
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childpos = rightpos;
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}
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/* Move the smaller child up. */
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tmp = PyList_GET_ITEM(heap, childpos);
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Py_INCREF(tmp);
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Py_DECREF(PyList_GET_ITEM(heap, pos));
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PyList_SET_ITEM(heap, pos, tmp);
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pos = childpos;
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childpos = 2*pos + 1;
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}
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/* The leaf at pos is empty now. Put newitem there, and and bubble
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it up to its final resting place (by sifting its parents down). */
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Py_DECREF(PyList_GET_ITEM(heap, pos));
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PyList_SET_ITEM(heap, pos, newitem);
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return _siftdown(heap, startpos, pos);
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}
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static PyObject *
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heappush(PyObject *self, PyObject *args)
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{
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PyObject *heap, *item;
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if (!PyArg_UnpackTuple(args, "heappush", 2, 2, &heap, &item))
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return NULL;
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if (!PyList_Check(heap)) {
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PyErr_SetString(PyExc_TypeError, "heap argument must be a list");
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return NULL;
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}
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if (PyList_Append(heap, item) == -1)
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return NULL;
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if (_siftdown((PyListObject *)heap, 0, PyList_GET_SIZE(heap)-1) == -1)
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return NULL;
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Py_INCREF(Py_None);
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return Py_None;
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}
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PyDoc_STRVAR(heappush_doc,
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"Push item onto heap, maintaining the heap invariant.");
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static PyObject *
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heappop(PyObject *self, PyObject *heap)
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{
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PyObject *lastelt, *returnitem;
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int n;
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if (!PyList_Check(heap)) {
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PyErr_SetString(PyExc_TypeError, "heap argument must be a list");
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return NULL;
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}
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/* # raises appropriate IndexError if heap is empty */
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n = PyList_GET_SIZE(heap);
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if (n == 0) {
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PyErr_SetString(PyExc_IndexError, "index out of range");
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return NULL;
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}
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lastelt = PyList_GET_ITEM(heap, n-1) ;
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Py_INCREF(lastelt);
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PyList_SetSlice(heap, n-1, n, NULL);
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n--;
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if (!n)
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return lastelt;
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returnitem = PyList_GET_ITEM(heap, 0);
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PyList_SET_ITEM(heap, 0, lastelt);
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if (_siftup((PyListObject *)heap, 0) == -1) {
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Py_DECREF(returnitem);
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return NULL;
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}
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return returnitem;
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}
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PyDoc_STRVAR(heappop_doc,
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"Pop the smallest item off the heap, maintaining the heap invariant.");
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static PyObject *
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heapreplace(PyObject *self, PyObject *args)
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{
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PyObject *heap, *item, *returnitem;
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if (!PyArg_UnpackTuple(args, "heapreplace", 2, 2, &heap, &item))
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return NULL;
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if (!PyList_Check(heap)) {
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PyErr_SetString(PyExc_TypeError, "heap argument must be a list");
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return NULL;
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}
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if (PyList_GET_SIZE(heap) < 1) {
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PyErr_SetString(PyExc_IndexError, "index out of range");
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return NULL;
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}
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returnitem = PyList_GET_ITEM(heap, 0);
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Py_INCREF(item);
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PyList_SET_ITEM(heap, 0, item);
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if (_siftup((PyListObject *)heap, 0) == -1) {
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Py_DECREF(returnitem);
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return NULL;
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}
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return returnitem;
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}
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PyDoc_STRVAR(heapreplace_doc,
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"Pop and return the current smallest value, and add the new item.\n\
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\n\
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This is more efficient than heappop() followed by heappush(), and can be\n\
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more appropriate when using a fixed-size heap. Note that the value\n\
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returned may be larger than item! That constrains reasonable uses of\n\
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this routine.\n");
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static PyObject *
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heapify(PyObject *self, PyObject *heap)
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{
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int i, n;
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if (!PyList_Check(heap)) {
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PyErr_SetString(PyExc_TypeError, "heap argument must be a list");
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return NULL;
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}
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n = PyList_GET_SIZE(heap);
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/* Transform bottom-up. The largest index there's any point to
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looking at is the largest with a child index in-range, so must
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have 2*i + 1 < n, or i < (n-1)/2. If n is even = 2*j, this is
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(2*j-1)/2 = j-1/2 so j-1 is the largest, which is n//2 - 1. If
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n is odd = 2*j+1, this is (2*j+1-1)/2 = j so j-1 is the largest,
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and that's again n//2-1.
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*/
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for (i=n/2-1 ; i>=0 ; i--)
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if(_siftup((PyListObject *)heap, i) == -1)
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return NULL;
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Py_INCREF(Py_None);
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return Py_None;
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}
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PyDoc_STRVAR(heapify_doc,
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"Transform list into a heap, in-place, in O(len(heap)) time.");
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static PyMethodDef heapq_methods[] = {
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{"heappush", (PyCFunction)heappush,
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METH_VARARGS, heappush_doc},
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{"heappop", (PyCFunction)heappop,
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METH_O, heappop_doc},
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{"heapreplace", (PyCFunction)heapreplace,
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METH_VARARGS, heapreplace_doc},
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{"heapify", (PyCFunction)heapify,
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METH_O, heapify_doc},
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{NULL, NULL} /* sentinel */
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};
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PyDoc_STRVAR(module_doc,
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"Heap queue algorithm (a.k.a. priority queue).\n\
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\n\
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Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for\n\
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all k, counting elements from 0. For the sake of comparison,\n\
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non-existing elements are considered to be infinite. The interesting\n\
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property of a heap is that a[0] is always its smallest element.\n\
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\n\
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Usage:\n\
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\n\
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heap = [] # creates an empty heap\n\
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heappush(heap, item) # pushes a new item on the heap\n\
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item = heappop(heap) # pops the smallest item from the heap\n\
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item = heap[0] # smallest item on the heap without popping it\n\
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heapify(x) # transforms list into a heap, in-place, in linear time\n\
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item = heapreplace(heap, item) # pops and returns smallest item, and adds\n\
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# new item; the heap size is unchanged\n\
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\n\
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Our API differs from textbook heap algorithms as follows:\n\
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\n\
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- We use 0-based indexing. This makes the relationship between the\n\
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index for a node and the indexes for its children slightly less\n\
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obvious, but is more suitable since Python uses 0-based indexing.\n\
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\n\
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- Our heappop() method returns the smallest item, not the largest.\n\
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\n\
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These two make it possible to view the heap as a regular Python list\n\
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without surprises: heap[0] is the smallest item, and heap.sort()\n\
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maintains the heap invariant!\n");
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PyDoc_STRVAR(__about__,
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"Heap queues\n\
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\n\
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[explanation by François Pinard]\n\
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\n\
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Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for\n\
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all k, counting elements from 0. For the sake of comparison,\n\
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non-existing elements are considered to be infinite. The interesting\n\
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property of a heap is that a[0] is always its smallest element.\n"
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"\n\
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The strange invariant above is meant to be an efficient memory\n\
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representation for a tournament. The numbers below are `k', not a[k]:\n\
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\n\
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0\n\
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\n\
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1 2\n\
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\n\
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3 4 5 6\n\
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\n\
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7 8 9 10 11 12 13 14\n\
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\n\
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15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30\n\
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\n\
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\n\
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In the tree above, each cell `k' is topping `2*k+1' and `2*k+2'. In\n\
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an usual binary tournament we see in sports, each cell is the winner\n\
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over the two cells it tops, and we can trace the winner down the tree\n\
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to see all opponents s/he had. However, in many computer applications\n\
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of such tournaments, we do not need to trace the history of a winner.\n\
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To be more memory efficient, when a winner is promoted, we try to\n\
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replace it by something else at a lower level, and the rule becomes\n\
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that a cell and the two cells it tops contain three different items,\n\
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but the top cell \"wins\" over the two topped cells.\n"
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"\n\
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If this heap invariant is protected at all time, index 0 is clearly\n\
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the overall winner. The simplest algorithmic way to remove it and\n\
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find the \"next\" winner is to move some loser (let's say cell 30 in the\n\
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diagram above) into the 0 position, and then percolate this new 0 down\n\
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the tree, exchanging values, until the invariant is re-established.\n\
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This is clearly logarithmic on the total number of items in the tree.\n\
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By iterating over all items, you get an O(n ln n) sort.\n"
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"\n\
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A nice feature of this sort is that you can efficiently insert new\n\
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items while the sort is going on, provided that the inserted items are\n\
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not \"better\" than the last 0'th element you extracted. This is\n\
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especially useful in simulation contexts, where the tree holds all\n\
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incoming events, and the \"win\" condition means the smallest scheduled\n\
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time. When an event schedule other events for execution, they are\n\
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scheduled into the future, so they can easily go into the heap. So, a\n\
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heap is a good structure for implementing schedulers (this is what I\n\
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used for my MIDI sequencer :-).\n"
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"\n\
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Various structures for implementing schedulers have been extensively\n\
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studied, and heaps are good for this, as they are reasonably speedy,\n\
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the speed is almost constant, and the worst case is not much different\n\
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than the average case. However, there are other representations which\n\
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are more efficient overall, yet the worst cases might be terrible.\n"
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"\n\
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Heaps are also very useful in big disk sorts. You most probably all\n\
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know that a big sort implies producing \"runs\" (which are pre-sorted\n\
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sequences, which size is usually related to the amount of CPU memory),\n\
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followed by a merging passes for these runs, which merging is often\n\
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very cleverly organised[1]. It is very important that the initial\n\
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sort produces the longest runs possible. Tournaments are a good way\n\
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to that. If, using all the memory available to hold a tournament, you\n\
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replace and percolate items that happen to fit the current run, you'll\n\
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produce runs which are twice the size of the memory for random input,\n\
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and much better for input fuzzily ordered.\n"
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"\n\
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Moreover, if you output the 0'th item on disk and get an input which\n\
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may not fit in the current tournament (because the value \"wins\" over\n\
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the last output value), it cannot fit in the heap, so the size of the\n\
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heap decreases. The freed memory could be cleverly reused immediately\n\
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for progressively building a second heap, which grows at exactly the\n\
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same rate the first heap is melting. When the first heap completely\n\
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vanishes, you switch heaps and start a new run. Clever and quite\n\
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effective!\n\
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\n\
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In a word, heaps are useful memory structures to know. I use them in\n\
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a few applications, and I think it is good to keep a `heap' module\n\
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around. :-)\n"
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"\n\
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--------------------\n\
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[1] The disk balancing algorithms which are current, nowadays, are\n\
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more annoying than clever, and this is a consequence of the seeking\n\
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capabilities of the disks. On devices which cannot seek, like big\n\
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tape drives, the story was quite different, and one had to be very\n\
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clever to ensure (far in advance) that each tape movement will be the\n\
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most effective possible (that is, will best participate at\n\
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\"progressing\" the merge). Some tapes were even able to read\n\
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backwards, and this was also used to avoid the rewinding time.\n\
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Believe me, real good tape sorts were quite spectacular to watch!\n\
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From all times, sorting has always been a Great Art! :-)\n");
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PyMODINIT_FUNC
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initheapq(void)
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{
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PyObject *m;
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m = Py_InitModule3("heapq", heapq_methods, module_doc);
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PyModule_AddObject(m, "__about__", PyString_FromString(__about__));
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}
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