pyopengl-users

[PyOpenGL-Users] stackoverflow: fastest way to build a c array from a list of tuples of floats?

[Jonathan H. <ta...@ta...>]

There's an interesting question on StackOverflow today that I suspect a 
lot of people would like to see an optimal answer to:

-----
What is the fastest way in python to build a c array from a list of 
tuples of floats? The context: my Python code pass arrays of 2D vertices 
to OpenGL.

I tested 2 approaches, one with ctypes, the other with struct, the 
latter being more than twice faster.

from random import random
points = [(random(), random()) for _ in xrange(1000)]

from ctypes import c_float
def array_ctypes(points):
     n = len(points)
     return n, (c_float*(2*n))(*[u for point in points for u in point])

from struct import pack
def array_struct(points):
     n = len(points)
     return n, pack("f"*2*n, *[u for point in points for u in point])

Any other alternatives?
-----
http://stackoverflow.com/q/4156872/10176


He's already chosen one answer, but I think that was premature, and some 
of you folks might have substantially better ideas. I'm thinking of a 
fully static-typed Cython function, but I apparently lack the smarts to 
get it working over breakfast, and now I have to go to work.

	Jonathan

-- 
Jonathan Hartley      Made of meat.      http://tartley.com
ta...@ta...   +44 7737 062 225   twitter/skype: tartley

Re: [PyOpenGL-Users] stackoverflow: fastest way to build a c array from a list of tuples of floats?

[Christopher B. <Chr...@no...>]

On 11/11/10 11:48 PM, Jonathan Hartley wrote:
> What is the fastest way in python to build a c array from a list of
> tuples of floats? The context: my Python code pass arrays of 2D vertices
> to OpenGL.

This seems like an obvious use for numpy:

> points = [(random(), random()) for _ in xrange(1000)]

def array_numpy(points):
     return np.array(points, dtype=float32)

And you can save the overhead of the function call, since it's a 
one-liner anyway. Of course, if you're using numpy, you probably never 
would have had all those points in tuples in the first place -- you'd 
just use numpy arrays from the start.

> Any other alternatives?

There's also array.array:

def array_array(points):
     a = array.array('f')
     [a.extend(p) for p in points]
     return a

Which brings up a question I have: is there way to write a "nothing 
comprehension"? -- I use the list comprehension syntax, as it's a nice 
clean way to process all the items in a list -- but I don't need the 
resulting list -- it seems there should be a way to write that without 
the overhead of creating a useless list.

-Chris


> -----
> http://stackoverflow.com/q/4156872/10176
>
>
> He's already chosen one answer, but I think that was premature, and some
> of you folks might have substantially better ideas. I'm thinking of a
> fully static-typed Cython function, but I apparently lack the smarts to
> get it working over breakfast, and now I have to go to work.
>
> 	Jonathan
>


-- 
Christopher Barker, Ph.D.
Oceanographer

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Chr...@no...

Re: [PyOpenGL-Users] stackoverflow: fastest way to build a c array from a list of tuples of floats?

[Toby D. <to...@ta...>]

On Friday 12 Nov 2010, Christopher Barker wrote:
> def array_array(points):
>      a = array.array('f')
>      [a.extend(p) for p in points]
>      return a
> 
> Which brings up a question I have: is there way to write a "nothing
> comprehension"? 

	for p in points: a.extend(p)

-- 
Toby Dickenson

Re: [PyOpenGL-Users] stackoverflow: fastest way to build a c array from a list of tuples of floats?

[Jonathan H. <ta...@ta...>]

On 12/11/2010 16:29, Christopher Barker wrote:
> On 11/11/10 11:48 PM, Jonathan Hartley wrote:
>> What is the fastest way in python to build a c array from a list of
>> tuples of floats? The context: my Python code pass arrays of 2D vertices
>> to OpenGL.
>
> This seems like an obvious use for numpy:
>
>> points = [(random(), random()) for _ in xrange(1000)]
>
> def array_numpy(points):
>       return np.array(points, dtype=float32)
>
> And you can save the overhead of the function call, since it's a
> one-liner anyway. Of course, if you're using numpy, you probably never
> would have had all those points in tuples in the first place -- you'd
> just use numpy arrays from the start.




Thanks for this. I'm trying out the numpy approach now.

	Jonathan
-- 
Jonathan Hartley      Made of meat.      http://tartley.com
ta...@ta...   +44 7737 062 225   twitter/skype: tartley