Building an interleaved buffer for pyopengl and numpy
- by Nick Sonneveld
I'm trying to batch up a bunch of vertices and texture coords in an interleaved array before sending it to pyOpengl's glInterleavedArrays/glDrawArrays. The only problem is that I'm unable to find a suitably fast enough way to append data into a numpy array.
Is there a better way to do this? I would have thought it would be quicker to preallocate the array and then fill it with data but instead, generating a python list and converting it to a numpy array is "faster". Although 15ms for 4096 quads seems slow.
I have included some example code and their timings.
#!/usr/bin/python
import timeit
import numpy
import ctypes
import random
USE_RANDOM=True
USE_STATIC_BUFFER=True
STATIC_BUFFER = numpy.empty(4096*20, dtype=numpy.float32)
def render(i):
# pretend these are different each time
if USE_RANDOM:
tex_left, tex_right, tex_top, tex_bottom = random.random(), random.random(), random.random(), random.random()
left, right, top, bottom = random.random(), random.random(), random.random(), random.random()
else:
tex_left, tex_right, tex_top, tex_bottom = 0.0, 1.0, 1.0, 0.0
left, right, top, bottom = -1.0, 1.0, 1.0, -1.0
ibuffer = (
tex_left, tex_bottom, left, bottom, 0.0, # Lower left corner
tex_right, tex_bottom, right, bottom, 0.0, # Lower right corner
tex_right, tex_top, right, top, 0.0, # Upper right corner
tex_left, tex_top, left, top, 0.0, # upper left
)
return ibuffer
# create python list.. convert to numpy array at end
def create_array_1():
ibuffer = []
for x in xrange(4096):
data = render(x)
ibuffer += data
ibuffer = numpy.array(ibuffer, dtype=numpy.float32)
return ibuffer
# numpy.array, placing individually by index
def create_array_2():
if USE_STATIC_BUFFER:
ibuffer = STATIC_BUFFER
else:
ibuffer = numpy.empty(4096*20, dtype=numpy.float32)
index = 0
for x in xrange(4096):
data = render(x)
for v in data:
ibuffer[index] = v
index += 1
return ibuffer
# using slicing
def create_array_3():
if USE_STATIC_BUFFER:
ibuffer = STATIC_BUFFER
else:
ibuffer = numpy.empty(4096*20, dtype=numpy.float32)
index = 0
for x in xrange(4096):
data = render(x)
ibuffer[index:index+20] = data
index += 20
return ibuffer
# using numpy.concat on a list of ibuffers
def create_array_4():
ibuffer_concat = []
for x in xrange(4096):
data = render(x)
# converting makes a diff!
data = numpy.array(data, dtype=numpy.float32)
ibuffer_concat.append(data)
return numpy.concatenate(ibuffer_concat)
# using numpy array.put
def create_array_5():
if USE_STATIC_BUFFER:
ibuffer = STATIC_BUFFER
else:
ibuffer = numpy.empty(4096*20, dtype=numpy.float32)
index = 0
for x in xrange(4096):
data = render(x)
ibuffer.put( xrange(index, index+20), data)
index += 20
return ibuffer
# using ctype array
CTYPES_ARRAY = ctypes.c_float*(4096*20)
def create_array_6():
ibuffer = []
for x in xrange(4096):
data = render(x)
ibuffer += data
ibuffer = CTYPES_ARRAY(*ibuffer)
return ibuffer
def equals(a, b):
for i,v in enumerate(a):
if b[i] != v:
return False
return True
if __name__ == "__main__":
number = 100
# if random, don't try and compare arrays
if not USE_RANDOM and not USE_STATIC_BUFFER:
a = create_array_1()
assert equals( a, create_array_2() )
assert equals( a, create_array_3() )
assert equals( a, create_array_4() )
assert equals( a, create_array_5() )
assert equals( a, create_array_6() )
t = timeit.Timer( "testing2.create_array_1()", "import testing2" )
print 'from list:', t.timeit(number)/number*1000.0, 'ms'
t = timeit.Timer( "testing2.create_array_2()", "import testing2" )
print 'array: indexed:', t.timeit(number)/number*1000.0, 'ms'
t = timeit.Timer( "testing2.create_array_3()", "import testing2" )
print 'array: slicing:', t.timeit(number)/number*1000.0, 'ms'
t = timeit.Timer( "testing2.create_array_4()", "import testing2" )
print 'array: concat:', t.timeit(number)/number*1000.0, 'ms'
t = timeit.Timer( "testing2.create_array_5()", "import testing2" )
print 'array: put:', t.timeit(number)/number*1000.0, 'ms'
t = timeit.Timer( "testing2.create_array_6()", "import testing2" )
print 'ctypes float array:', t.timeit(number)/number*1000.0, 'ms'
Timings using random numbers:
$ python testing2.py
from list: 15.0486779213 ms
array: indexed: 24.8184704781 ms
array: slicing: 50.2214789391 ms
array: concat: 44.1691994667 ms
array: put: 73.5879898071 ms
ctypes float array: 20.6674289703 ms
edit note: changed code to produce random numbers for each render to reduce object reuse and to simulate different vertices each time.
edit note2: added static buffer and force all numpy.empty() to use dtype=float32
note 1/Apr/2010: still no progress and I don't really feel that any of the answers have solved the problem yet.
