From 1660e0c1f162efcc2a19e07ca87193e071bca311 Mon Sep 17 00:00:00 2001 From: Raymond Hettinger Date: Mon, 19 Apr 2004 19:21:43 +0000 Subject: * Restore the pure python version of heapq.py. * Mark the C version as private and only use when available. --- Modules/heapqmodule.c | 364 -------------------------------------------------- 1 file changed, 364 deletions(-) delete mode 100644 Modules/heapqmodule.c 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__)); -} - -- cgit v0.12