"I'm a bit unhappy with your code, because it's so hard to read tbh.
You don't like objects?"
I would phrase this differently now: I think you can improve your code
by using objects when they are appropriate. :-)
2010/3/6 Ian Mallett :
> Unfortunately, the pure Python implementation is actually a
Sent prematurely, typed tab and then space :-(. Real message work in progress.
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I'm a bit unhappy with your code, because it's so hard to read tbh.
You don't like objects?
2010/3/6 Ian Mallett :
> Unfortunately, the pure Python implementation is actually an order of
> magnitude faster. The fastest solution right now is to use numpy for the
> transformations, then convert it
On Sat, Mar 6, 2010 at 12:03 PM, Friedrich Romstedt <
friedrichromst...@gmail.com> wrote:
> At the moment, I can do nothing about that. Seems that we have
> reached the limit. Anyhow, is it now faster than your Python list
> implementation, and if yes, how much? How large was your gain by
> usi
2010/3/6 Ian Mallett :
> On Sat, Mar 6, 2010 at 1:20 AM, Friedrich Romstedt
> wrote:
>> Hmm. Let's see ... Can you tell me how I can test the time calls in a
>> script take? I have no idea.
>
> I've been doing:
> t1 = time.time()
> #code
> t2 = time.time()
> print "[code]:",t2-t1
Ok. I just wo
Hi,
On Sat, Mar 6, 2010 at 1:20 AM, Friedrich Romstedt <
friedrichromst...@gmail.com> wrote:
> Hmm. Let's see ... Can you tell me how I can test the time calls in a
> script take? I have no idea.
>
I've been doing:
t1 = time.time()
#code
t2 = time.time()
print "[code]:",t2-t1
> > #0.0840001106
2010/3/5 Ian Mallett :
> #takes 0.04 seconds
> inner = np.inner(ns, v1s - some_point)
Ok, I don't know why I was able to overlook this:
dotprod = (ns * (v1s - some_point)).sum(axis = 1)
The things with the inner product have been deleted.
Now I will really *attach* it ...
Hope it's faster,
Fri
2010/3/5 Ian Mallett :
> Cool--this works perfectly now :-)
:-)
> Unfortunately, it's actually slower :P Most of the slowest part is in the
> removing doubles section.
Hmm. Let's see ... Can you tell me how I can test the time calls in a
script take? I have no idea.
> #takes 0.04 seconds
> i
Cool--this works perfectly now :-)
Unfortunately, it's actually slower :P Most of the slowest part is in the
removing doubles section.
Some of the costliest calls:
#takes 0.04 seconds
inner = np.inner(ns, v1s - some_point)
#0.0840001106262
sum_1 = sum.reshape((len(sum), 1)).repeat(len(sum), ax
Do you have doublets in the v_array?
In case not, then you owe me a donut.
See attachment.
Friedrich
P.S.: You misunderstood too, the line you wanted to change was in
context to detect back-facing triangles, and there one vertex is
sufficient.
shading.py
Description: Binary data
_
Firstly, I want to thank you for all the time and attention you've obviously
put into this code.
On Tue, Mar 2, 2010 at 12:27 AM, Friedrich Romstedt <
friedrichromst...@gmail.com> wrote:
> The loop I can replace by numpy operations:
>
> >>> v_array
> array([[1, 2, 3],
> [4, 5, 6],
> [
I'm learning too by answering your questions.
2010/3/2 Ian Mallett :
> 1 v_array #array of beautifully transformed vertices from last step
> 2 n_array #array of beautifully transformed normals from last step
> 3 f_list #list of vec4s, where every vec4 is three vertex indices and a
> 4
Excellent--this setup works perfectly! In the areas I was concentrating on,
the the speed increased an order of magnitude.
However, the overall speed seems to have dropped. I believe this may be
because the heavy indexing that follows on the result is slower in numpy.
Is this a correct analysis?
This is how I always do it:
In [1]: import numpy as np
In [3]: tmat = np.array([[0., 1., 0., 5.],[0., 0., 1., 3.],[1., 0., 0.,
2.]])
In [4]: tmat
Out[4]:
array([[ 0., 1., 0., 5.],
[ 0., 0., 1., 3.],
[ 1., 0., 0., 2.]])
In [5]: points = np.random.random((5, 3))
In
2010/3/1 Charles R Harris :
> On Sun, Feb 28, 2010 at 7:58 PM, Ian Mallett wrote:
>> Excellent--and a 3D rotation matrix is 3x3--so the list can remain n*3.
>> Now the question is how to apply a rotation matrix to the array of vec3?
>
> It looks like you want something like
>
> res = dot(vec, rot)
On Sun, Feb 28, 2010 at 7:58 PM, Ian Mallett wrote:
> On Sun, Feb 28, 2010 at 6:54 PM, Charles R Harris <
> charlesr.har...@gmail.com> wrote:
>
>> Why not just add a vector to get translation? There is no need to go the
>> homogeneous form. Or you can just leave the vectors at length 4 and use a
On Sun, Feb 28, 2010 at 6:54 PM, Charles R Harris wrote:
> Why not just add a vector to get translation? There is no need to go the
> homogeneous form. Or you can just leave the vectors at length 4 and use a
> slice to access the first three components. That way you can leave the ones
> in place.
On Sun, Feb 28, 2010 at 7:35 PM, Ian Mallett wrote:
> On Sun, Feb 28, 2010 at 6:31 PM, Charles R Harris <
> charlesr.har...@gmail.com> wrote:
>
>> As I understand it, you want *different* matrices applied to each vector?
>
> Nope--I need the same matrix applied to each vector.
>
> Because 3D tran
On Sun, Feb 28, 2010 at 6:31 PM, Charles R Harris wrote:
> As I understand it, you want *different* matrices applied to each vector?
Nope--I need the same matrix applied to each vector.
Because 3D translation matrices must, if I understand correctly be 4x4, the
vectors must first be changed to
On Sun, Feb 28, 2010 at 5:53 PM, Ian Mallett wrote:
> Hi,
>
> I have a list of vec3 lists (e.g. [[1,2,3],[4,5,6],[7,8,9],...]). To every
> single one of the vec3 sublists, I am currently applying transformations. I
> need to optimize this with numpy.
>
> To get proper results, as far as I can te
On Sun, Feb 28, 2010 at 7:53 PM, Ian Mallett wrote:
> Hi,
>
> I have a list of vec3 lists (e.g. [[1,2,3],[4,5,6],[7,8,9],...]). To every
> single one of the vec3 sublists, I am currently applying transformations. I
> need to optimize this with numpy.
>
> To get proper results, as far as I can tel
Hi,
I have a list of vec3 lists (e.g. [[1,2,3],[4,5,6],[7,8,9],...]). To every
single one of the vec3 sublists, I am currently applying transformations. I
need to optimize this with numpy.
To get proper results, as far as I can tell, the vec3 lists must be
expressed as vec4s: [[1,2,3],[4,5,6],[7
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