diff options
| -rw-r--r-- | Lib/heapq.py | 261 | ||||
| -rw-r--r-- | Modules/_heapmodule.c | 364 | ||||
| -rw-r--r-- | PC/config.c | 4 | ||||
| -rw-r--r-- | setup.py | 2 |
4 files changed, 628 insertions, 3 deletions
diff --git a/Lib/heapq.py b/Lib/heapq.py new file mode 100644 index 0000000..3eb69d8 --- /dev/null +++ b/Lib/heapq.py @@ -0,0 +1,261 @@ +# -*- coding: Latin-1 -*- + +"""Heap queue algorithm (a.k.a. priority queue). + +Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for +all k, counting elements from 0. For the sake of comparison, +non-existing elements are considered to be infinite. The interesting +property of a heap is that a[0] is always its smallest element. + +Usage: + +heap = [] # creates an empty heap +heappush(heap, item) # pushes a new item on the heap +item = heappop(heap) # pops the smallest item from the heap +item = heap[0] # smallest item on the heap without popping it +heapify(x) # transforms list into a heap, in-place, in linear time +item = heapreplace(heap, item) # pops and returns smallest item, and adds + # new item; the heap size is unchanged + +Our API differs from textbook heap algorithms as follows: + +- We use 0-based indexing. This makes the relationship between the + index for a node and the indexes for its children slightly less + obvious, but is more suitable since Python uses 0-based indexing. + +- Our heappop() method returns the smallest item, not the largest. + +These two make it possible to view the heap as a regular Python list +without surprises: heap[0] is the smallest item, and heap.sort() +maintains the heap invariant! +""" + +# Original code by Kevin O'Connor, augmented by Tim Peters + +__about__ = """Heap queues + +[explanation by François Pinard] + +Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for +all k, counting elements from 0. For the sake of comparison, +non-existing elements are considered to be infinite. The interesting +property of a heap is that a[0] is always its smallest element. + +The strange invariant above is meant to be an efficient memory +representation for a tournament. The numbers below are `k', not a[k]: + + 0 + + 1 2 + + 3 4 5 6 + + 7 8 9 10 11 12 13 14 + + 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 + + +In the tree above, each cell `k' is topping `2*k+1' and `2*k+2'. In +an usual binary tournament we see in sports, each cell is the winner +over the two cells it tops, and we can trace the winner down the tree +to see all opponents s/he had. However, in many computer applications +of such tournaments, we do not need to trace the history of a winner. +To be more memory efficient, when a winner is promoted, we try to +replace it by something else at a lower level, and the rule becomes +that a cell and the two cells it tops contain three different items, +but the top cell "wins" over the two topped cells. + +If this heap invariant is protected at all time, index 0 is clearly +the overall winner. The simplest algorithmic way to remove it and +find the "next" winner is to move some loser (let's say cell 30 in the +diagram above) into the 0 position, and then percolate this new 0 down +the tree, exchanging values, until the invariant is re-established. +This is clearly logarithmic on the total number of items in the tree. +By iterating over all items, you get an O(n ln n) sort. + +A nice feature of this sort is that you can efficiently insert new +items while the sort is going on, provided that the inserted items are +not "better" than the last 0'th element you extracted. This is +especially useful in simulation contexts, where the tree holds all +incoming events, and the "win" condition means the smallest scheduled +time. When an event schedule other events for execution, they are +scheduled into the future, so they can easily go into the heap. So, a +heap is a good structure for implementing schedulers (this is what I +used for my MIDI sequencer :-). + +Various structures for implementing schedulers have been extensively +studied, and heaps are good for this, as they are reasonably speedy, +the speed is almost constant, and the worst case is not much different +than the average case. However, there are other representations which +are more efficient overall, yet the worst cases might be terrible. + +Heaps are also very useful in big disk sorts. You most probably all +know that a big sort implies producing "runs" (which are pre-sorted +sequences, which size is usually related to the amount of CPU memory), +followed by a merging passes for these runs, which merging is often +very cleverly organised[1]. It is very important that the initial +sort produces the longest runs possible. Tournaments are a good way +to that. If, using all the memory available to hold a tournament, you +replace and percolate items that happen to fit the current run, you'll +produce runs which are twice the size of the memory for random input, +and much better for input fuzzily ordered. + +Moreover, if you output the 0'th item on disk and get an input which +may not fit in the current tournament (because the value "wins" over +the last output value), it cannot fit in the heap, so the size of the +heap decreases. The freed memory could be cleverly reused immediately +for progressively building a second heap, which grows at exactly the +same rate the first heap is melting. When the first heap completely +vanishes, you switch heaps and start a new run. Clever and quite +effective! + +In a word, heaps are useful memory structures to know. I use them in +a few applications, and I think it is good to keep a `heap' module +around. :-) + +-------------------- +[1] The disk balancing algorithms which are current, nowadays, are +more annoying than clever, and this is a consequence of the seeking +capabilities of the disks. On devices which cannot seek, like big +tape drives, the story was quite different, and one had to be very +clever to ensure (far in advance) that each tape movement will be the +most effective possible (that is, will best participate at +"progressing" the merge). Some tapes were even able to read +backwards, and this was also used to avoid the rewinding time. +Believe me, real good tape sorts were quite spectacular to watch! +From all times, sorting has always been a Great Art! :-) +""" + +__all__ = ['heappush', 'heappop', 'heapify', 'heapreplace'] + +def heappush(heap, item): + """Push item onto heap, maintaining the heap invariant.""" + heap.append(item) + _siftdown(heap, 0, len(heap)-1) + +def heappop(heap): + """Pop the smallest item off the heap, maintaining the heap invariant.""" + lastelt = heap.pop() # raises appropriate IndexError if heap is empty + if heap: + returnitem = heap[0] + heap[0] = lastelt + _siftup(heap, 0) + else: + returnitem = lastelt + return returnitem + +def heapreplace(heap, item): + """Pop and return the current smallest value, and add the new item. + + This is more efficient than heappop() followed by heappush(), and can be + more appropriate when using a fixed-size heap. Note that the value + returned may be larger than item! That constrains reasonable uses of + this routine. + """ + returnitem = heap[0] # raises appropriate IndexError if heap is empty + heap[0] = item + _siftup(heap, 0) + return returnitem + +def heapify(x): + """Transform list into a heap, in-place, in O(len(heap)) time.""" + n = len(x) + # 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 in reversed(xrange(n//2)): + _siftup(x, i) + +# 'heap' is a heap at all indices >= startpos, except possibly for pos. pos +# is the index of a leaf with a possibly out-of-order value. Restore the +# heap invariant. +def _siftdown(heap, startpos, pos): + newitem = heap[pos] + # Follow the path to the root, moving parents down until finding a place + # newitem fits. + while pos > startpos: + parentpos = (pos - 1) >> 1 + parent = heap[parentpos] + if parent <= newitem: + break + heap[pos] = parent + pos = parentpos + heap[pos] = newitem + +# The child indices of heap index pos are already heaps, and we want to make +# a heap at index pos too. We do this by bubbling the smaller child of +# pos up (and so on with that child's children, etc) until hitting a leaf, +# then using _siftdown to move the oddball originally at index pos into place. +# +# We *could* break out of the loop as soon as we find a pos where newitem <= +# both its children, but turns out that's not a good idea, and despite that +# many books write the algorithm that way. During a heap pop, the last array +# element is sifted in, and that tends to be large, so that comparing it +# against values starting from the root usually doesn't pay (= usually doesn't +# get us out of the loop early). See Knuth, Volume 3, where this is +# explained and quantified in an exercise. +# +# Cutting the # of comparisons is important, since these routines have no +# way to extract "the priority" from an array element, so that intelligence +# is likely to be hiding in custom __cmp__ methods, or in array elements +# storing (priority, record) tuples. Comparisons are thus potentially +# expensive. +# +# On random arrays of length 1000, making this change cut the number of +# comparisons made by heapify() a little, and those made by exhaustive +# heappop() a lot, in accord with theory. Here are typical results from 3 +# runs (3 just to demonstrate how small the variance is): +# +# Compares needed by heapify Compares needed by 1000 heappops +# -------------------------- -------------------------------- +# 1837 cut to 1663 14996 cut to 8680 +# 1855 cut to 1659 14966 cut to 8678 +# 1847 cut to 1660 15024 cut to 8703 +# +# Building the heap by using heappush() 1000 times instead required +# 2198, 2148, and 2219 compares: heapify() is more efficient, when +# you can use it. +# +# The total compares needed by list.sort() on the same lists were 8627, +# 8627, and 8632 (this should be compared to the sum of heapify() and +# heappop() compares): list.sort() is (unsurprisingly!) more efficient +# for sorting. + +def _siftup(heap, pos): + endpos = len(heap) + startpos = pos + newitem = heap[pos] + # 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 and heap[rightpos] <= heap[childpos]: + childpos = rightpos + # Move the smaller child up. + heap[pos] = heap[childpos] + pos = childpos + childpos = 2*pos + 1 + # The leaf at pos is empty now. Put newitem there, and bubble it up + # to its final resting place (by sifting its parents down). + heap[pos] = newitem + _siftdown(heap, startpos, pos) + +# If available, use C implementation +try: + from _heapq import heappush, heappop, heapify, heapreplace +except ImportError: + pass + +if __name__ == "__main__": + # Simple sanity test + heap = [] + data = [1, 3, 5, 7, 9, 2, 4, 6, 8, 0] + for item in data: + heappush(heap, item) + sort = [] + while heap: + sort.append(heappop(heap)) + print sort diff --git a/Modules/_heapmodule.c b/Modules/_heapmodule.c new file mode 100644 index 0000000..7455fbc --- /dev/null +++ b/Modules/_heapmodule.c @@ -0,0 +1,364 @@ +/* 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 +init_heapq(void) +{ + PyObject *m; + + m = Py_InitModule3("_heapq", heapq_methods, module_doc); + PyModule_AddObject(m, "__about__", PyString_FromString(__about__)); +} + diff --git a/PC/config.c b/PC/config.c index e618302..1b8ddf1 100644 --- a/PC/config.c +++ b/PC/config.c @@ -47,7 +47,7 @@ extern void initzipimport(void); extern void init_random(void); extern void inititertools(void); extern void initcollections(void); -extern void initheapq(void); +extern void init_heapq(void); extern void init_bisect(void); extern void init_symtable(void); extern void initmmap(void); @@ -135,7 +135,7 @@ struct _inittab _PyImport_Inittab[] = { {"_hotshot", init_hotshot}, {"_random", init_random}, {"_bisect", init_bisect}, - {"heapq", initheapq}, + {"_heapq", init_heapq}, {"itertools", inititertools}, {"collections", initcollections}, {"_symtable", init_symtable}, @@ -327,7 +327,7 @@ class PyBuildExt(build_ext): # bisect exts.append( Extension("_bisect", ["_bisectmodule.c"]) ) # heapq - exts.append( Extension("heapq", ["heapqmodule.c"]) ) + exts.append( Extension("_heapq", ["_heapqmodule.c"]) ) # operator.add() and similar goodies exts.append( Extension('operator', ['operator.c']) ) # Python C API test module |