diff options
Diffstat (limited to 'AppPkg/Applications/Python/Python-2.7.2/Modules/_heapqmodule.c')
| -rw-r--r-- | AppPkg/Applications/Python/Python-2.7.2/Modules/_heapqmodule.c | 697 |
1 files changed, 697 insertions, 0 deletions
diff --git a/AppPkg/Applications/Python/Python-2.7.2/Modules/_heapqmodule.c b/AppPkg/Applications/Python/Python-2.7.2/Modules/_heapqmodule.c new file mode 100644 index 0000000000..c5bfc4517a --- /dev/null +++ b/AppPkg/Applications/Python/Python-2.7.2/Modules/_heapqmodule.c @@ -0,0 +1,697 @@ +/* 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"
+
+/* Older implementations of heapq used Py_LE for comparisons. Now, it uses
+ Py_LT so it will match min(), sorted(), and bisect(). Unfortunately, some
+ client code (Twisted for example) relied on Py_LE, so this little function
+ restores compatibility by trying both.
+*/
+static int
+cmp_lt(PyObject *x, PyObject *y)
+{
+ int cmp;
+ static PyObject *lt = NULL;
+
+ if (lt == NULL) {
+ lt = PyString_FromString("__lt__");
+ if (lt == NULL)
+ return -1;
+ }
+ if (PyObject_HasAttr(x, lt))
+ return PyObject_RichCompareBool(x, y, Py_LT);
+ cmp = PyObject_RichCompareBool(y, x, Py_LE);
+ if (cmp != -1)
+ cmp = 1 - cmp;
+ return cmp;
+}
+
+static int
+_siftdown(PyListObject *heap, Py_ssize_t startpos, Py_ssize_t pos)
+{
+ PyObject *newitem, *parent;
+ int cmp;
+ Py_ssize_t 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 = cmp_lt(newitem, parent);
+ if (cmp == -1) {
+ Py_DECREF(newitem);
+ return -1;
+ }
+ if (cmp == 0)
+ 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, Py_ssize_t pos)
+{
+ Py_ssize_t 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 = cmp_lt(
+ PyList_GET_ITEM(heap, childpos),
+ PyList_GET_ITEM(heap, rightpos));
+ if (cmp == -1) {
+ Py_DECREF(newitem);
+ return -1;
+ }
+ if (cmp == 0)
+ 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;
+ Py_ssize_t 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 unless written as part of a conditional replacement:\n\n\
+ if item > heap[0]:\n\
+ item = heapreplace(heap, item)\n");
+
+static PyObject *
+heappushpop(PyObject *self, PyObject *args)
+{
+ PyObject *heap, *item, *returnitem;
+ int cmp;
+
+ if (!PyArg_UnpackTuple(args, "heappushpop", 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) {
+ Py_INCREF(item);
+ return item;
+ }
+
+ cmp = cmp_lt(PyList_GET_ITEM(heap, 0), item);
+ if (cmp == -1)
+ return NULL;
+ if (cmp == 0) {
+ Py_INCREF(item);
+ return item;
+ }
+
+ 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(heappushpop_doc,
+"Push item on the heap, then pop and return the smallest item\n\
+from the heap. The combined action runs more efficiently than\n\
+heappush() followed by a separate call to heappop().");
+
+static PyObject *
+heapify(PyObject *self, PyObject *heap)
+{
+ Py_ssize_t 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 PyObject *
+nlargest(PyObject *self, PyObject *args)
+{
+ PyObject *heap=NULL, *elem, *iterable, *sol, *it, *oldelem;
+ Py_ssize_t i, n;
+ int cmp;
+
+ if (!PyArg_ParseTuple(args, "nO:nlargest", &n, &iterable))
+ return NULL;
+
+ it = PyObject_GetIter(iterable);
+ if (it == NULL)
+ return NULL;
+
+ heap = PyList_New(0);
+ if (heap == NULL)
+ goto fail;
+
+ for (i=0 ; i<n ; i++ ){
+ elem = PyIter_Next(it);
+ if (elem == NULL) {
+ if (PyErr_Occurred())
+ goto fail;
+ else
+ goto sortit;
+ }
+ if (PyList_Append(heap, elem) == -1) {
+ Py_DECREF(elem);
+ goto fail;
+ }
+ Py_DECREF(elem);
+ }
+ if (PyList_GET_SIZE(heap) == 0)
+ goto sortit;
+
+ for (i=n/2-1 ; i>=0 ; i--)
+ if(_siftup((PyListObject *)heap, i) == -1)
+ goto fail;
+
+ sol = PyList_GET_ITEM(heap, 0);
+ while (1) {
+ elem = PyIter_Next(it);
+ if (elem == NULL) {
+ if (PyErr_Occurred())
+ goto fail;
+ else
+ goto sortit;
+ }
+ cmp = cmp_lt(sol, elem);
+ if (cmp == -1) {
+ Py_DECREF(elem);
+ goto fail;
+ }
+ if (cmp == 0) {
+ Py_DECREF(elem);
+ continue;
+ }
+ oldelem = PyList_GET_ITEM(heap, 0);
+ PyList_SET_ITEM(heap, 0, elem);
+ Py_DECREF(oldelem);
+ if (_siftup((PyListObject *)heap, 0) == -1)
+ goto fail;
+ sol = PyList_GET_ITEM(heap, 0);
+ }
+sortit:
+ if (PyList_Sort(heap) == -1)
+ goto fail;
+ if (PyList_Reverse(heap) == -1)
+ goto fail;
+ Py_DECREF(it);
+ return heap;
+
+fail:
+ Py_DECREF(it);
+ Py_XDECREF(heap);
+ return NULL;
+}
+
+PyDoc_STRVAR(nlargest_doc,
+"Find the n largest elements in a dataset.\n\
+\n\
+Equivalent to: sorted(iterable, reverse=True)[:n]\n");
+
+static int
+_siftdownmax(PyListObject *heap, Py_ssize_t startpos, Py_ssize_t pos)
+{
+ PyObject *newitem, *parent;
+ int cmp;
+ Py_ssize_t 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 = cmp_lt(parent, newitem);
+ if (cmp == -1) {
+ Py_DECREF(newitem);
+ return -1;
+ }
+ if (cmp == 0)
+ 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
+_siftupmax(PyListObject *heap, Py_ssize_t pos)
+{
+ Py_ssize_t 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 = cmp_lt(
+ PyList_GET_ITEM(heap, rightpos),
+ PyList_GET_ITEM(heap, childpos));
+ if (cmp == -1) {
+ Py_DECREF(newitem);
+ return -1;
+ }
+ if (cmp == 0)
+ 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 _siftdownmax(heap, startpos, pos);
+}
+
+static PyObject *
+nsmallest(PyObject *self, PyObject *args)
+{
+ PyObject *heap=NULL, *elem, *iterable, *los, *it, *oldelem;
+ Py_ssize_t i, n;
+ int cmp;
+
+ if (!PyArg_ParseTuple(args, "nO:nsmallest", &n, &iterable))
+ return NULL;
+
+ it = PyObject_GetIter(iterable);
+ if (it == NULL)
+ return NULL;
+
+ heap = PyList_New(0);
+ if (heap == NULL)
+ goto fail;
+
+ for (i=0 ; i<n ; i++ ){
+ elem = PyIter_Next(it);
+ if (elem == NULL) {
+ if (PyErr_Occurred())
+ goto fail;
+ else
+ goto sortit;
+ }
+ if (PyList_Append(heap, elem) == -1) {
+ Py_DECREF(elem);
+ goto fail;
+ }
+ Py_DECREF(elem);
+ }
+ n = PyList_GET_SIZE(heap);
+ if (n == 0)
+ goto sortit;
+
+ for (i=n/2-1 ; i>=0 ; i--)
+ if(_siftupmax((PyListObject *)heap, i) == -1)
+ goto fail;
+
+ los = PyList_GET_ITEM(heap, 0);
+ while (1) {
+ elem = PyIter_Next(it);
+ if (elem == NULL) {
+ if (PyErr_Occurred())
+ goto fail;
+ else
+ goto sortit;
+ }
+ cmp = cmp_lt(elem, los);
+ if (cmp == -1) {
+ Py_DECREF(elem);
+ goto fail;
+ }
+ if (cmp == 0) {
+ Py_DECREF(elem);
+ continue;
+ }
+
+ oldelem = PyList_GET_ITEM(heap, 0);
+ PyList_SET_ITEM(heap, 0, elem);
+ Py_DECREF(oldelem);
+ if (_siftupmax((PyListObject *)heap, 0) == -1)
+ goto fail;
+ los = PyList_GET_ITEM(heap, 0);
+ }
+
+sortit:
+ if (PyList_Sort(heap) == -1)
+ goto fail;
+ Py_DECREF(it);
+ return heap;
+
+fail:
+ Py_DECREF(it);
+ Py_XDECREF(heap);
+ return NULL;
+}
+
+PyDoc_STRVAR(nsmallest_doc,
+"Find the n smallest elements in a dataset.\n\
+\n\
+Equivalent to: sorted(iterable)[:n]\n");
+
+static PyMethodDef heapq_methods[] = {
+ {"heappush", (PyCFunction)heappush,
+ METH_VARARGS, heappush_doc},
+ {"heappushpop", (PyCFunction)heappushpop,
+ METH_VARARGS, heappushpop_doc},
+ {"heappop", (PyCFunction)heappop,
+ METH_O, heappop_doc},
+ {"heapreplace", (PyCFunction)heapreplace,
+ METH_VARARGS, heapreplace_doc},
+ {"heapify", (PyCFunction)heapify,
+ METH_O, heapify_doc},
+ {"nlargest", (PyCFunction)nlargest,
+ METH_VARARGS, nlargest_doc},
+ {"nsmallest", (PyCFunction)nsmallest,
+ METH_VARARGS, nsmallest_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
+init_heapq(void)
+{
+ PyObject *m;
+
+ m = Py_InitModule3("_heapq", heapq_methods, module_doc);
+ if (m == NULL)
+ return;
+ PyModule_AddObject(m, "__about__", PyString_FromString(__about__));
+}
+
|