A simple example of a heap data structure as a C extension. This data structure can store and sort any python object that has a comparison function (i.e. strings, numbers, etc.).
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 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 | #include "Python.h"
#include "structmember.h"
/* This is an example type extension. This is based loosely on a data
* structure described in 'Mastering Algorithms with C'. It allocates memory
* for the underlying array in "rows" instead of on an element-by-element
* basis. This does make it faster than the element-by-element allocating
* version, but the difference is hard to measure until you get up into the
* millions of elements.
*
* This heap can be used with any Python object that implements a __cmp__
* function. I used the Python memory management functions and tried to make
* this object blend in as much as possible to the Python framework.
*
* Right now the only way to figure out how many items are on the hash is to
* get the 'size' member of the hash (or keep count).
*/
/*----------------------------------------------------------------------------*
* Heap structure *
*----------------------------------------------------------------------------*/
typedef struct {
PyObject_HEAD
int size; /* number of pointer elements held */
int minsize; /* minimum number of allocated elements in cache */
int allocsize; /* currently allocated size of cache */
PyObject **tree; /* the cache */
} Heap;
/*----------------------------------------------------------------------------*
* Heap push *
*----------------------------------------------------------------------------*/
static PyObject *
Heap_push(PyObject *self, PyObject *arg)
{
Heap *heap = (Heap *)self;
PyObject **new_tree; /* new heap if needed */
PyObject *temp; /* swap placeholder */
int cpos; /* current position */
int ppos; /* parent position */
int newsize; /* size placeholder */
/* check to see if there is enough space allocated for the object */
if (heap->size + 1 > heap->allocsize)
{
/* the size of the next row is always one plus the total size */
newsize = 2*heap->allocsize + 1;
/* allocate storage for the new node */
new_tree = (PyObject **)PyMem_Realloc(heap->tree, newsize * sizeof(PyObject *));
if (new_tree == NULL)
{
PyErr_SetString(PyExc_MemoryError, "unable to increase heap storage");
return NULL;
}
else
/* connect your new data structure and record the allocated size */
heap->allocsize = newsize;
heap->tree = new_tree;
}
/* insert the node after the last node */
Py_INCREF(arg);
heap->tree[heap->size] = arg;
/* heapify the tree by pushing the contents of the new node upwards */
cpos = heap->size;
ppos = (cpos - 1)/2;
while (cpos > 0 && PyObject_Compare(heap->tree[ppos], heap->tree[cpos]) < 0)
{
temp = heap->tree[ppos];
heap->tree[ppos] = heap->tree[cpos];
heap->tree[cpos] = temp;
/* move up one layer in the tree and continue heapifying */
cpos = ppos;
ppos = (cpos - 1)/2;
}
/* adjust the size of the heap to account for the inserted data */
heap->size++;
Py_INCREF(Py_None);
return Py_None;
}
/*----------------------------------------------------------------------------*
* Heap pop *
*----------------------------------------------------------------------------*/
static PyObject *
Heap_pop(PyObject *self)
{
Heap *heap = (Heap *)self;
PyObject **new_tree; /* new heap if needed */
PyObject *data; /* the top node */
PyObject *temp; /* swap placeholder */
int ipos; /* initial position */
int lpos; /* left position */
int rpos; /* right position */
int mpos; /* modified position */
int newsize; /* size placeholder */
/* do not allow extraction from an empty heap */
if (heap->size == 0)
{
PyErr_SetString(PyExc_IndexError, "attempt to pop from empty heap");
return NULL;
}
/* extract the first node, move the last node to the top of the heap */
data = heap->tree[0];
temp = heap->tree[heap->size - 1];
heap->tree[0] = temp;
/* check to see if you need to make the heap storage smaller */
newsize = (heap->allocsize - 1)/2;
if ((heap->size - 1 <= newsize) && (newsize >= heap->minsize))
{
new_tree = (PyObject **)PyMem_Realloc(heap->tree, newsize*sizeof(PyObject *));
if (new_tree == NULL)
{
PyErr_SetString(PyExc_MemoryError, "unable to decrease heap storage");
return NULL;
}
else
{
/* connect your new data structure and record the allocated size */
heap->allocsize = newsize;
heap->tree = new_tree;
}
}
/* since you have pointers to the top and bottom nodes, decrease size */
heap->size--;
/* heapify the storage object by pushing the top node down */
ipos = 0;
while (1)
{
/* select the child to swap with the current node */
lpos = ipos*2 + 1;
rpos = ipos*2 + 2;
if (lpos < heap->size && PyObject_Compare(heap->tree[lpos], heap->tree[ipos]) > 0)
mpos = lpos;
else
mpos = ipos;
if (rpos < heap->size && PyObject_Compare(heap->tree[rpos], heap->tree[mpos]) > 0)
mpos = rpos;
/* when mpos is ipos, the heap property has been restored */
if (mpos == ipos)
break;
else
{
/* swap the contents of the current node and the selected child */
temp = heap->tree[mpos];
heap->tree[mpos] = heap->tree[ipos];
heap->tree[ipos] = temp;
}
/* move down one level and continue heapifying */
ipos = mpos;
}
return data;
}
/*----------------------------------------------------------------------------*
* Heap destructor *
*----------------------------------------------------------------------------*/
static void
Heap_dealloc(PyObject *self)
{
int i;
Heap *heap = (Heap *)self;
/* remove references to everything on the heap */
for ( i = 0; i < heap->size; i++ )
Py_DECREF(heap->tree[i]);
/* free the memory used by the pointers on the heap, if it exists */
if (heap->tree != NULL)
PyMem_Free(heap->tree);
/* now free the type object */
self->ob_type->tp_free(self);
}
/*----------------------------------------------------------------------------*
* Heap constructor *
*----------------------------------------------------------------------------*/
static PyObject *
Heap_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
{
Heap *self;
PyObject **tree;
/* initialize an empty heap */
self = (Heap *)type->tp_alloc(type, 0);
self->size = 0; /* number of currently held objects */
self->minsize = 256; /* minimum number of allocated objects */
tree = (PyObject **)PyMem_Malloc(self->minsize*sizeof(PyObject *));
if (tree == NULL)
{
/* this is pretty much fatal - return an error and exit */
PyErr_SetString(PyExc_MemoryError, "unable to allocate heap storage");
self->allocsize = 0;
return NULL;
}
else
{
/* return a new Heap */
self->tree = tree;
self->allocsize = self->minsize;
return (PyObject *)self;
}
}
/*----------------------------------------------------------------------------*
* Heap members *
*----------------------------------------------------------------------------*/
static PyMemberDef Heap_members[] = {
{"size", T_INT, offsetof(Heap, size), 0, "number of objects in the heap"},
{NULL} /* sentinel */
};
static PyMethodDef Heap_methods[] = {
{"push", (PyCFunction)Heap_push, METH_O, "push an object onto the heap"},
{"pop", (PyCFunction)Heap_pop, METH_NOARGS, "pop the top object from the heap"},
{NULL} /* sentinel */
};
static PyTypeObject HeapType = {
PyObject_HEAD_INIT(NULL)
0, /* ob_size */
"Heap", /* tp_name */
sizeof(Heap), /* tp_basicsize */
0, /* tp_itemsize */
(destructor)Heap_dealloc, /* tp_dealloc */
0, /* tp_print */
0, /* tp_getattr */
0, /* tp_setattr */
0, /* tp_compare */
0, /* tp_repr */
0, /* tp_as_number */
0, /* tp_as_sequence */
0, /* tp_as_mapping */
0, /* tp_hash */
0, /* tp_call */
0, /* tp_str */
0, /* tp_getattro*/
0, /* tp_setattro*/
0, /* tp_as_buffer*/
Py_TPFLAGS_DEFAULT, /* tp_flags*/
"Heap objects", /* tp_doc */
0, /* tp_traverse */
0, /* tp_clear */
0, /* tp_richcompare */
0, /* tp_weaklistoffset */
0, /* tp_iter */
0, /* tp_iternext */
Heap_methods, /* tp_methods */
Heap_members, /* tp_members */
0, /* tp_getset */
0, /* tp_base */
0, /* tp_dict */
0, /* tp_descr_get */
0, /* tp_descr_set */
0, /* tp_dictoffset */
0, /* tp_init */
0, /* tp_alloc */
Heap_new, /* tp_new */
};
static PyMethodDef module_methods[] = {
{NULL} /* sentinel */
};
/* declarations for DLL export */
#ifndef PyMODINIT_FUNC
#define PyMODINIT_FUNC void
#endif
PyMODINIT_FUNC
initheap(void)
{
PyObject* m;
if (PyType_Ready(&HeapType) < 0)
return;
m = Py_InitModule3("heap", module_methods, "Example module that implements a heap");
if (m == NULL)
return;
Py_INCREF(&HeapType);
PyModule_AddObject(m, "Heap", (PyObject *)&HeapType);
}
|
Heaps (Priority Queues) are used in many algorithms (minimum spanning trees, etc). I was interested in writing extension modules and was looking for something simple to try out. Most of what I learned was from the Python C API, the Extending and Embedding tutorial, and this website. I have tried this code out on Linux and Windows 2000 by using the distutils module (an example setup.py file follows):
from distutils.core import Extension, setup setup (name="heap", version="1.0", maintainer="Tim Meehan", description="simple Python extention type", ext_modules=[Extension("heap", ["heap.c",])] )
Here is an example of the Heap in action on my windows machine (it works on more than just numbers, I just used numbers as an example): ActivePython 2.2.2 Build 224 (ActiveState Corp.) based on Python 2.2.2 (#37, Nov 26 2002, 10:24:37) [MSC 32 bit (Intel)] on win32 Type "help", "copyright", "credits" or "license" for more information.
>>> from heap import Heap
>>> from random import random
>>> a = Heap()
>>> a.pop()
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IndexError: attempt to pop from empty heap
>>> for i in range(10):
... a.push(random())
...
>>> for i in range(10):
... a.pop()
...
0.89993181004742606
0.82018958943326037
0.63165609014853752
0.52421761109388232
0.43681731273589941
0.3583793139350786
0.20696096641639428
0.11730573937621958
0.097850757250128151
0.066719074253705379
>>>