Terry Reedy wrote:

>

Robert LaMarca wrote:
>Hi,

I am using numpy and wish to create very large arrays. My system is

AMD 64 x 2 Ubuntu 8.04. Ubuntu should be 64 bit. I have 3gb RAM and a

15 GB swap drive.

The command I have been trying to use is;

g=numpy.ones([1000,1000,1000],numpy.int32)

This returns a memory error. A smaller array ([500,500,500]) worked

fine.. Two smaller arrays again crashed the system.

So... I did the math. a 1000x1000x1000 array at 32 bits should be

around 4gb RAM... Obviously larger than RAM, but much smaller than the

swap drive.

1. So... does Numpy have a really lot of overhead? Or is my system

just not somehow getting to make use of the 15gb swap area. 2. Is

there a way I can access the swap area, or direct numpy to do so? Or

do I have to write out my own numpy cache system... 3. How difficult

is it to use data compression internally on numpy arrays?

I do not know what numpy does, but constant arrays only need to store

the dimensions and the constant value and have a getitem method that

returns that constant value for any valid index. This is at most a few

hundred bytes regardless of the dimensions.

Presumably, he's using numpy.ones() as an example of creating a large array, not

because he actually needs an array full of 1s.

--

Robert Kern

"I have come to believe that the whole world is an enigma, a harmless enigma

that is made terrible by our own mad attempt to interpret it as though it had

an underlying truth."

-- Umberto Eco