| /* 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, Py_ssize_t startpos, Py_ssize_t pos) |
| { |
| PyObject *newitem, *parent; |
| Py_ssize_t parentpos, size; |
| int cmp; |
| |
| assert(PyList_Check(heap)); |
| size = PyList_GET_SIZE(heap); |
| if (pos >= size) { |
| PyErr_SetString(PyExc_IndexError, "index out of range"); |
| return -1; |
| } |
| |
| /* Follow the path to the root, moving parents down until finding |
| a place newitem fits. */ |
| newitem = PyList_GET_ITEM(heap, pos); |
| while (pos > startpos) { |
| parentpos = (pos - 1) >> 1; |
| parent = PyList_GET_ITEM(heap, parentpos); |
| cmp = PyObject_RichCompareBool(newitem, parent, Py_LT); |
| if (cmp == -1) |
| return -1; |
| if (size != PyList_GET_SIZE(heap)) { |
| PyErr_SetString(PyExc_RuntimeError, |
| "list changed size during iteration"); |
| return -1; |
| } |
| if (cmp == 0) |
| break; |
| parent = PyList_GET_ITEM(heap, parentpos); |
| newitem = PyList_GET_ITEM(heap, pos); |
| PyList_SET_ITEM(heap, parentpos, newitem); |
| PyList_SET_ITEM(heap, pos, parent); |
| pos = parentpos; |
| } |
| return 0; |
| } |
| |
| static int |
| _siftup(PyListObject *heap, Py_ssize_t pos) |
| { |
| Py_ssize_t startpos, endpos, childpos, rightpos, limit; |
| PyObject *tmp1, *tmp2; |
| int cmp; |
| |
| assert(PyList_Check(heap)); |
| endpos = PyList_GET_SIZE(heap); |
| startpos = pos; |
| if (pos >= endpos) { |
| PyErr_SetString(PyExc_IndexError, "index out of range"); |
| return -1; |
| } |
| |
| /* Bubble up the smaller child until hitting a leaf. */ |
| limit = endpos / 2; /* smallest pos that has no child */ |
| while (pos < limit) { |
| /* Set childpos to index of smaller child. */ |
| childpos = 2*pos + 1; /* leftmost child position */ |
| rightpos = childpos + 1; |
| if (rightpos < endpos) { |
| cmp = PyObject_RichCompareBool( |
| PyList_GET_ITEM(heap, childpos), |
| PyList_GET_ITEM(heap, rightpos), |
| Py_LT); |
| if (cmp == -1) |
| return -1; |
| if (cmp == 0) |
| childpos = rightpos; |
| if (endpos != PyList_GET_SIZE(heap)) { |
| PyErr_SetString(PyExc_RuntimeError, |
| "list changed size during iteration"); |
| return -1; |
| } |
| } |
| /* Move the smaller child up. */ |
| tmp1 = PyList_GET_ITEM(heap, childpos); |
| tmp2 = PyList_GET_ITEM(heap, pos); |
| PyList_SET_ITEM(heap, childpos, tmp2); |
| PyList_SET_ITEM(heap, pos, tmp1); |
| pos = childpos; |
| } |
| /* Bubble it up to its final resting place (by sifting its parents down). */ |
| 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, |
| "heappush(heap, item) -> None. 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); |
| if (PyList_SetSlice(heap, n-1, n, NULL) < 0) { |
| Py_DECREF(lastelt); |
| return 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, |
| "heapreplace(heap, item) -> value. 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 = PyObject_RichCompareBool(PyList_GET_ITEM(heap, 0), item, Py_LT); |
| if (cmp == -1) |
| return NULL; |
| if (cmp == 0) { |
| Py_INCREF(item); |
| return item; |
| } |
| |
| if (PyList_GET_SIZE(heap) == 0) { |
| 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(heappushpop_doc, |
| "heappushpop(heap, item) -> value. 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 = PyObject_RichCompareBool(sol, elem, Py_LT); |
| 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 = PyObject_RichCompareBool(parent, newitem, Py_LT); |
| 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, limit; |
| 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. */ |
| limit = endpos / 2; /* smallest pos that has no child */ |
| while (pos < limit) { |
| /* Set childpos to index of smaller child. */ |
| childpos = 2*pos + 1; /* leftmost child position */ |
| rightpos = childpos + 1; |
| if (rightpos < endpos) { |
| cmp = PyObject_RichCompareBool( |
| PyList_GET_ITEM(heap, rightpos), |
| PyList_GET_ITEM(heap, childpos), |
| Py_LT); |
| 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; |
| } |
| |
| /* The leaf at pos is empty now. Put newitem there, 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 = PyObject_RichCompareBool(elem, los, Py_LT); |
| 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 Fran\xc3\xa7ois 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"); |
| |
| |
| static struct PyModuleDef _heapqmodule = { |
| PyModuleDef_HEAD_INIT, |
| "_heapq", |
| module_doc, |
| -1, |
| heapq_methods, |
| NULL, |
| NULL, |
| NULL, |
| NULL |
| }; |
| |
| PyMODINIT_FUNC |
| PyInit__heapq(void) |
| { |
| PyObject *m, *about; |
| |
| m = PyModule_Create(&_heapqmodule); |
| if (m == NULL) |
| return NULL; |
| about = PyUnicode_DecodeUTF8(__about__, strlen(__about__), NULL); |
| PyModule_AddObject(m, "__about__", about); |
| return m; |
| } |
| |