On 3/14/2007 2:46 PM, Robert Cimrman wrote:
> a = []
> while ...
> a.append( scalar )
> a = array( a )
While it may help, growing Python lists is also an O(N) process.
One can reduce the amount of allocations by preallocating an ndarray of
a certain size (e.g. 1024 scalars), filling it up, and storing it in a
linked list. Finally, the stored arrays are retrieved as a single
contiguous array. Example code below (cf. class scalar_list).
Sturla Molden
import numpy
class ndarray_list:
""" a single linked list of numpy ndarrays."""
class node:
def __init__(self, data):
self.next = None
self.data = data
def __init__(self):
self.head = None
self.tail = None
self.len = 0
def append(self, data):
tmp = self.node(data)
if self.tail == None:
self.tail = tmp
self.head = tmp
self.len = len(data)
else:
self.tail.next = tmp
self.tail = tmp
self.len += len(data)
def length(self): return self.len
def flatten(self, dtype=float):
tmp = numpy.empty(self.len, dtype=dtype)
cur = self.head
idx0 = 0
while cur:
tmp[idx0:idx0+len(cur.data)] = cur.data
idx0 += len(cur.data)
cur = cur.next
return tmp
class scalar_list:
""" a single linked list of numpy scalars."""
def __init__(self, size=1024, dtype=float):
self.cur = 0
self.size = size
self.dtype = dtype
self.arr = numpy.empty(size,dtype)
self.arrlist = ndarray_list()
def append(self, scalar):
cur = self.cur
self.arr[cur] = scalar
self.cur += 1
if self.cur == self.size:
self.arrlist.append(self.arr)
self.arr = numpy.empty(self.size,self.dtype)
self.cur = 0
def array(self):
if self.cur: self.arrlist.append(self.arr[:self.cur])
retval = self.arrlist.flatten(self.dtype)
self.cur = 0
self.arr = numpy.empty(self.size,self.dtype)
self.arrlist = ndarray_list()
self.arrlist.append(retval.copy())
return retval
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