Re: [Paraview] Ubuntu crash on large file load

2013-10-09 Thread pwhiteho
Thanks Burlen, No Crash with 4.0.1 ... all's well for now ...


From: Burlen Loring [blor...@lbl.gov]
Sent: Friday, October 04, 2013 12:32 PM
To: pwhiteho
Cc: paraview@paraview.org
Subject: Re: [Paraview] Ubuntu crash on large file load

Any chance you could verify that paraview 4.0.1 still has the problem?

if yes then post any error messages you have in the console. If there are none 
then you'll need to capture a backtrace or share some data and steps to 
reproduce the issue.

On 10/04/2013 08:57 AM, pwhiteho wrote:
Loading 1.7G of vtp and vtr files crashes ParaView 3.14 on machine with 48G of 
ram running under Ubuntu 12.04 - haven't explored threshold, but smaller total 
file sizes work fine ...

Loading same 1.7G on iMac (Intel Core i7; OSX 10.7.5; 16G ram) into ParaView 
3.14 through 4.01 opens fine.

What should I look for? ... static/dynamic application memory? ... not sure of 
graphics card and driver or other hardware specs on Ubuntu machine, but can get 
if needed ...



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[Paraview] Ubuntu crash on large file load

2013-10-04 Thread pwhiteho
Loading 1.7G of vtp and vtr files crashes ParaView 3.14 on machine with 48G of 
ram running under Ubuntu 12.04 - haven't explored threshold, but smaller total 
file sizes work fine ...

Loading same 1.7G on iMac (Intel Core i7; OSX 10.7.5; 16G ram) into ParaView 
3.14 through 4.01 opens fine.

What should I look for? ... static/dynamic application memory? ... not sure of 
graphics card and driver or other hardware specs on Ubuntu machine, but can get 
if needed ...
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Re: [Paraview] Stream Tracer in eigenvector field

2013-08-27 Thread pwhiteho
Burlen,

Not really off target - it needed to be checked. And thanks for the heads-up on 
the hyperstreamline plugin - the FixVector routine is exactly it: they 
interpolate the field tensor, compute it's eigenvectors, and fix the stream 
propagation if the eigenvector's orientation is flipped.

I've played with applying a tube filter to a streamline in a vector field - 
color from one eigenvalue and cross-section from the other two - to get a 
hyperstreamline, but a combination of the StreamTracer and HyperStreamline 
filters would be ideal: direct application to tensor fields and orientation 
correction as in HyperStreamline with seeding and streamlines from StreamTracer.

With regard to your glyph orientation issue: Forgive me if you already know 
this, but if you look at http://en.wikipedia.org/wiki/Determinant you will see 
that, geometrically, the determinant of a 2x2 matrix is just an oriented 
parallelogram. The determinant of a 3x3 matrix is equal to the volume of a 
parallelepiped. They don't say, but I believe the faces are the geometric 
equivalent of the minors of the 3x3, to which the interpretation of the 2x2 
determinant applies. Again they don't mention, but volumes have orientations 
determined by the orientation of their faces ... so your negative determinants 
give an orientation reflected by the inward-pointing normals ... apparently 
reflected in the glyph construction ... eh?


From: Burlen Loring [blor...@lbl.gov]
Sent: Saturday, August 24, 2013 7:58 PM
To: pwhiteho
Cc: Andy Bauer; paraview@paraview.org
Subject: Re: [Paraview] Stream Tracer in eigenvector field

Paul,

Sorry to have given such an off target answer! Your idea about checking the dot 
product as you progress makes sense. That's how VTK's a hyper stream tracer 
handles the issue. I wonder if you can make use of it? Bringing VTK classes 
into PV isn't too hard but parallelizing them can be challenging.

Burlen

On 08/24/2013 12:27 PM, Burlen Loring wrote:
in the case of the glyphing algorithm it's the surface normals of the 
transformed glyph that have the relation to the sign determinant of the 
transform matrix. this transformation matrix is constructed from the 
eigenvectors directly. at any rate, good luck.

On 08/23/2013 06:35 PM, pwhiteho wrote:
Burlen, you had me hoping, but alas ... there is no direct correlation between 
the determinant ( a.k.a. the 3rd tensor invariant ) and the orientation of the 
computed eigenvectors. You did get me thinking there may be other 
possibilities. I'm sure I can use the dot product in a marching cube scheme to 
enforce a consistent orientation progression and generate a vector field 
ParaView will handle well. It'll just have to be robust enough to handle a true 
change near some sharp topological feature.

Thanks,
Paul


From: Burlen Loring [blor...@lbl.govmailto:blor...@lbl.gov]
Sent: Friday, August 23, 2013 4:31 PM
To: Andy Bauer; pwhiteho
Cc: paraview@paraview.orgmailto:paraview@paraview.org
Subject: Re: [Paraview] Stream Tracer in eigenvector field

Eigenvectors are unique up to a constant so if you took any eigenvector and 
multiplied it by -1 it's still an eigenvector. You could see it in the 
definition,

M x=\lambda x

eigenvector x appears in both sides of the eqn.

I had a similar problem with tensor glyphs in ParaView. In that case I was able 
to solve by looking at the sign of the determinant of the transformation matrix 
(see bug report, patch and mail list posts below). I wonder if you could 
adapt/build on this solution there to solve your issue here?

http://vtk.org/Bug/view.php?id=12179
http://vtk.1045678.n5.nabble.com/tensor-glyph-inward-pointing-surface-normals-td4388361.html

Burlen


On 08/23/2013 01:10 PM, Andy Bauer wrote:
Hi Paul,

Apologies as my math is a bit rusty but isn't the sign of the eigenvector 
related to the sign of its corresponding eigenvalue? In that case if you make 
sure that all of the eigenvalues are positive then all of their corresponding 
eigenvectors should be aligned properly. If that's the case and you have access 
to the eigenvalues of the eigenvectors you could use the calculator or python 
calculator to properly orient the eigenvectors.

In any case, if you can come up with an algorithm that properly orients the 
eigenvectors you should be able to do that in the python calculator or 
calculator filters. If not, then things could get a bit hairy as far as 
computationally figuring out which is the proper direction your eigenvalues 
should have.

Regards,
Andy


On Fri, Aug 23, 2013 at 3:38 PM, pwhiteho 
pwhit...@masonlive.gmu.edumailto:pwhit...@masonlive.gmu.edu wrote:
The term eigenvector, used to describe the principal directions of a tensor, 
is a bit of a misnomer since it's not a vector as interpreted by the Stream 
Tracer filter - it's more accurately bi-directional like tension/compression 
and could be termed eigenaxis/eigenaxes. When interpreted

[Paraview] Stream Tracer in eigenvector field

2013-08-23 Thread pwhiteho
The term eigenvector, used to describe the principal directions of a tensor, 
is a bit of a misnomer since it's not a vector as interpreted by the Stream 
Tracer filter - it's more accurately bi-directional like tension/compression 
and could be termed eigenaxis/eigenaxes. When interpreted as a vector, there 
is an inherent sign ambiguity in each eigenvector - the sign is indeterminate 
and one is free to choose + or -, and that is exactly what Mathematica does ( 
likely true for other routines also ).

I've been using Mathematica to prototype computations for the investigation of 
tensor topology which I then visualize in ParaView. Eigen-decomposition of a 
tensor field at each grid point returns an orthonormal set of eigenvectors, 
uncorrelated with any neighbors. Taken separately, each eigenvector field 
exhibits large regions of smoothly varying orientation, but there are 
systematic and random reversals of orientation that confound the Stream Tracer 
filter, sending streamlines wandering around the field. What is needed is a 
true tangent curve ( tensor line ) integrator that would avoid doubling back 
as the streamline propagates, similar to the scheme of Weinstein, et. al., ( 
IEEE VIS'99 ) which computes the dot product of the incoming propagation vector 
with the eigenvector; and if near -1, negate the outgoing propagation vector. 
This can also be fancied-up to accommodate noisy initial tensor data as in 
Weinstein.

I think I would be taking on too much at this point in learning to write my own 
filter so have been exploring ways to pre-process the eigenvector fields before 
visualizing in Paraview, but I ask:
1. Have I missed something in existing filters that would handle this?
2. Can the existing Stream Tracer be modified?
3. Does the eigenvector routine in ParaView yield the same sign ambiguity among 
uncorrelated computations?

Thanks,
Paul W

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Re: [Paraview] Stream Tracer in eigenvector field

2013-08-23 Thread pwhiteho
Burlen, you had me hoping, but alas ... there is no direct correlation between 
the determinant ( a.k.a. the 3rd tensor invariant ) and the orientation of the 
computed eigenvectors. You did get me thinking there may be other 
possibilities. I'm sure I can use the dot product in a marching cube scheme to 
enforce a consistent orientation progression and generate a vector field 
ParaView will handle well. It'll just have to be robust enough to handle a true 
change near some sharp topological feature.

Thanks,
Paul


From: Burlen Loring [blor...@lbl.gov]
Sent: Friday, August 23, 2013 4:31 PM
To: Andy Bauer; pwhiteho
Cc: paraview@paraview.org
Subject: Re: [Paraview] Stream Tracer in eigenvector field

Eigenvectors are unique up to a constant so if you took any eigenvector and 
multiplied it by -1 it's still an eigenvector. You could see it in the 
definition,

M x=\lambda x

eigenvector x appears in both sides of the eqn.

I had a similar problem with tensor glyphs in ParaView. In that case I was able 
to solve by looking at the sign of the determinant of the transformation matrix 
(see bug report, patch and mail list posts below). I wonder if you could 
adapt/build on this solution there to solve your issue here?

http://vtk.org/Bug/view.php?id=12179
http://vtk.1045678.n5.nabble.com/tensor-glyph-inward-pointing-surface-normals-td4388361.html

Burlen


On 08/23/2013 01:10 PM, Andy Bauer wrote:
Hi Paul,

Apologies as my math is a bit rusty but isn't the sign of the eigenvector 
related to the sign of its corresponding eigenvalue? In that case if you make 
sure that all of the eigenvalues are positive then all of their corresponding 
eigenvectors should be aligned properly. If that's the case and you have access 
to the eigenvalues of the eigenvectors you could use the calculator or python 
calculator to properly orient the eigenvectors.

In any case, if you can come up with an algorithm that properly orients the 
eigenvectors you should be able to do that in the python calculator or 
calculator filters. If not, then things could get a bit hairy as far as 
computationally figuring out which is the proper direction your eigenvalues 
should have.

Regards,
Andy


On Fri, Aug 23, 2013 at 3:38 PM, pwhiteho 
pwhit...@masonlive.gmu.edumailto:pwhit...@masonlive.gmu.edu wrote:
The term eigenvector, used to describe the principal directions of a tensor, 
is a bit of a misnomer since it's not a vector as interpreted by the Stream 
Tracer filter - it's more accurately bi-directional like tension/compression 
and could be termed eigenaxis/eigenaxes. When interpreted as a vector, there 
is an inherent sign ambiguity in each eigenvector - the sign is indeterminate 
and one is free to choose + or -, and that is exactly what Mathematica does ( 
likely true for other routines also ).

I've been using Mathematica to prototype computations for the investigation of 
tensor topology which I then visualize in ParaView. Eigen-decomposition of a 
tensor field at each grid point returns an orthonormal set of eigenvectors, 
uncorrelated with any neighbors. Taken separately, each eigenvector field 
exhibits large regions of smoothly varying orientation, but there are 
systematic and random reversals of orientation that confound the Stream Tracer 
filter, sending streamlines wandering around the field. What is needed is a 
true tangent curve ( tensor line ) integrator that would avoid doubling back 
as the streamline propagates, similar to the scheme of Weinstein, et. al., ( 
IEEE VIS'99 ) which computes the dot product of the incoming propagation vector 
with the eigenvector; and if near -1, negate the outgoing propagation vector. 
This can also be fancied-up to accommodate noisy initial tensor data as in 
Weinstein.

I think I would be taking on too much at this point in learning to write my own 
filter so have been exploring ways to pre-process the eigenvector fields before 
visualizing in Paraview, but I ask:
1. Have I missed something in existing filters that would handle this?
2. Can the existing Stream Tracer be modified?
3. Does the eigenvector routine in ParaView yield the same sign ambiguity among 
uncorrelated computations?

Thanks,
Paul W


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