Closed jdemeyer closed 6 years ago
jdemeyer
commented
12 years ago Description changed:
---
+++
@@ -1,4 +1,4 @@
-This used to work on Solaris SPARC in sage-5.1.beta3, but no longer in sage-5.1.beta5:
+This used to work on Solaris SPARC in sage-5.1.beta3, but no longer in sage-5.1.beta4:
sage -t --long -force_lib devel/sage/sage/plot/plot3d/list_plot3d.py
Description changed:
---
+++
@@ -6,3 +6,11 @@
[5001.6 s]+ +More precisely, this takes forever: + +``` +Trying:
ppurka
commented
12 years ago This takes 0.18s on my laptop in sage-5.1.beta5. I don't see why it should take >5s on some other hardware unless the hardware is really that slow.
sage: l=[]
sage: for i in range(-5,5):
for j in range(-5,5):
l.append((normalvariate(0,1),normalvariate(0,1),normalvariate(0,1)))
sage: %time list_plot3d(l,interpolation_type='nn',texture='yellow',num_points=100)
CPU times: user 0.16 s, sys: 0.01 s, total: 0.18 s
Wall time: 0.18 sDescription changed:
---
+++
@@ -7,10 +7,10 @@
[5001.6 s]-More precisely, this takes forever: +More precisely, this takes a very long time:
Trying:
- list_plot3d(l,interpolation_type='nn',texture='yellow',num_points=Integer(100))###line 159:_sage_ >>> list_plot3d(l,interpolation_type='nn',texture='yellow',num_points=100)
+ list_plot3d([(Integer(0),Integer(0),Integer(1)), (Integer(2),Integer(3),Integer(4)), (-Integer(1),-Integer(1),-Integer(1))],num_points=Integer(400)) # long time###line 171:_sage_ >>> list_plot3d([(0,0,1), (2,3,4), (-1,-1,-1)],num_points=400) # long time
Expecting nothingReplying to @ppurka:
This takes 0.18s on my laptop in sage-5.1.beta5. I don't see why it should take >5s on some other hardware unless the hardware is really that slow.
Do an explaination of why it could have slowed down a lot because of this ticket?
ppurka
commented
12 years ago Replying to @jdemeyer:
Replying to @ppurka:
This takes 0.18s on my laptop in sage-5.1.beta5. I don't see why it should take >5s on some other hardware unless the hardware is really that slow.
Do an explaination of why it could have slowed down a lot because of this ticket?
I think the way to handle the slow down is to reduce the num_points to 100.
It is not really a "slow down" per se, but it actually gives an accurate plot. Before #13135 3d plots of lists of points were just plain wrong.
I think the following is a good means of checking the time that the plot in the revised description takes
sage: %time list_plot3d([(0,0,1), (2,3,4), (-1,-1,-1)],num_points=400).export_jmol('/tmp/a.jmol')
CPU times: user 5.37 s, sys: 0.05 s, total: 5.42 s
Wall time: 5.44 s
sage: %time list_plot3d([(0,0,1), (2,3,4), (-1,-1,-1)],num_points=100).export_jmol('/tmp/a.jmol')
CPU times: user 0.35 s, sys: 0.00 s, total: 0.35 s
Wall time: 0.35 sCan you run this command on the Solaris machine and find out what is the time taken? If it takes much less time on the latter command, then it would be good to have the latter one (with 100 points) instead of the former one (with 400 points).
jdemeyer
commented
12 years ago Here's an other weird thing: the test takes much longer when running the doctests than when run individually. Inside Sage, there is not much difference with/without the #12798 patches. But when running the doctest, there is a huge difference.
Could there be a resource leak of some kind?
ppurka
commented
12 years ago A resource leak might be a possibility. I ran the following two commands:
Vanilla sage-5.1beta5
...sage-5.1.beta5/devel/sage» ../../sage -t --long -force_lib sage/plot/plot3d/list_plot3d.py
sage -t --long -force_lib "devel/sage-main/sage/plot/plot3d/list_plot3d.py"
[10.3 s]
----------------------------------------------------------------------
All tests passed!
Total time for all tests: 10.3 secondsThen I changed that list_plot example to the following:
sage: list_plot3d([(0,0,1), (2,3,4), (-1,-1,-1)],num_points=400).export_jmol(tmp_filename()+'.jmol') # long timeAfter change to the list_plot example
...sage-5.1.beta5/devel/sage» ../../sage -t --long -force_lib sage/plot/plot3d/list_plot3d.py
sage -t --long -force_lib "devel/sage-main/sage/plot/plot3d/list_plot3d.py"
[8.6 s]
----------------------------------------------------------------------
All tests passed!
Total time for all tests: 8.6 secondsThat is quite a bit of a difference just for dumping the jmol into a file.
Is there some automated way to measure the memory/cpu usage of a running process?
jdemeyer
commented
12 years ago Description changed:
---
+++
@@ -1,4 +1,4 @@
-This used to work on Solaris SPARC in sage-5.1.beta3, but no longer in sage-5.1.beta4:
+#12798 causes this:
sage -t --long -force_lib devel/sage/sage/plot/plot3d/list_plot3d.py
Description changed:
---
+++
@@ -1,16 +1,12 @@
-#12798 causes this:
+#12798 and the matplotlib upgrade to 2.1.0 cause this:
-More precisely, this takes a very long time:
-``` -Trying:
`` +on hardware which is slow in computing withNaN` values.
Description changed:
---
+++
@@ -10,3 +10,5 @@
**********************************************************************on hardware which is slow in computing with NaN values.
+
+This plot is done by tachyon but in the input to tachyon, there are many NaNs.
Example tachyon input
jdemeyer
commented
6 years ago Attachment: tachyon.dat.gz
I'm attaching an example Tachyon input file generated by list_plot3d.
jdemeyer
commented
6 years ago Patch to show the problem
jdemeyer
commented
6 years ago Attachment: nancount.patch.gz
jdemeyer
commented
6 years ago Description changed:
---
+++
@@ -11,4 +11,34 @@on hardware which is slow in computing with NaN values.
-This plot is done by tachyon but in the input to tachyon, there are many NaNs.
+This plot is done by tachyon but in the input to tachyon, there are many NaNs. This can be seen by applying attachment: nancount.patch. The result is
+
+```
+sage -t --long src/sage/plot/plot3d/list_plot3d.py
+**
+File "src/sage/plot/plot3d/list_plot3d.py", line 143, in sage.plot.plot3d.list_plot3d.list_plot3d
+Failed example:
Description changed:
---
+++
@@ -1,4 +1,4 @@
-#12798 and the matplotlib upgrade to 2.1.0 cause this:
+Both #12798 and the matplotlib upgrade (#23696) cause this:
sage -t --long --warn-long 10.0 src/sage/plot/plot3d/list_plot3d.py @@ -42,3 +42,4 @@ Graphics3d Object
+In Description changed:
---
+++
@@ -42,4 +42,3 @@
Graphics3d Object
**********************************************************************-In
fchapoton
commented
6 years ago simpler case maybe
sage: var('x,y')
sage: f = lambda x,y: x+y if x*x+y*y<=1 else NaN
sage: plot3d(f,(x,-2,2),(y,-2,2)).tachyon() and
sage: G = polygon([(NaN,NaN,NaN)]*3)
sage: G.tachyon_repr(G.default_render_params())
['TRI V0 nan nan nan V1 nan nan nan V2 nan nan nan', 'texture3']Maybe we should filter this out of tachyon input ? or more generally ?
jdemeyer
commented
6 years ago Replying to @fchapoton:
Maybe we should filter this out of tachyon input ? or more generally ?
That doesn't solve the underlying problem. Why are NaNs suddenly appearing? That didn't happen before #23696.
strogdon
commented
6 years ago Doesn't explain the NaN but could the following be what one is observing when doctesting
sage: l = []
sage: for i in range(-5, 5):
....: for j in range(-5, 5):
....: l.append((normalvariate(0, 1), normalvariate(0, 1), normalvariate(0, 1)))
....:
sage: %timeit list_plot3d(l, interpolation_type='clough', texture='yellow', num_points=100)
The slowest run took 109.32 times longer than the fastest. This could mean that an intermediate result is being cached.
1 loop, best of 3: 3.11 ms per loopThe first run is always the slowest.
kiwifb
commented
6 years ago OK in the case of clough and linear we could able to filter NaN at a cost. Both define a function g like so
def g(x, y):
z = f([x, y])
return (x, y, z)I guess g could return empty if z turns to be a NaN. Is it even doable?
jdemeyer
commented
6 years ago Filtering NaNs is easy, but that is really ignoring the problem. The real problem is that NaNs are appearing in a computation which did not produce NaNs before #23696.
kiwifb
commented
6 years ago The interpolation algorithm is completely different, so there is a possibility that this particular algorithm is completely unsuited to this particular set of points. I am now curious to see what the plot look like but that will have to wait until I go back to work tomorrow morning (~14 hours in my time zone).
strogdon
commented
6 years ago It may be that the nan are a feature of using scipy.interpolate.CloughTocher2DInterpolator. The documentation indicates that if requested points are outside the convex hull then, unless specified, they are filled in with nan.
jdemeyer
commented
6 years ago Replying to @strogdon:
It may be that the
nanare a feature of using scipy.interpolate.CloughTocher2DInterpolator. The documentation indicates that if requested points are outside the convex hull then, unless specified, they are filled in withnan.
Thanks for the info. If so, filtering them probably is the right thing to do after all.
kiwifb
commented
6 years ago Replying to @jdemeyer:
Replying to @strogdon:
It may be that the
nanare a feature of using scipy.interpolate.CloughTocher2DInterpolator. The documentation indicates that if requested points are outside the convex hull then, unless specified, they are filled in withnan.Thanks for the info. If so, filtering them probably is the right thing to do after all.
Yes, this algorithm just doesn't do extrapolation and put NaN for anything that would be an extrapolation. We could set an arbitrary value, but to what? The average z value of the input? 0?
jdemeyer
commented
6 years ago Replying to @fchapoton:
Maybe we should filter this out of tachyon input ? or more generally ?
If you want to filter them, I think the right place is the triangulate() method.
kiwifb
commented
6 years ago I just spent some time with matplotlib code to check what it does for extrapolation. And it looks like it returns NaN for those points as well. So the problem should be present as well for linear interpolation. I couldn't find anything in the documentation about what the stuff behind spline do for extrapolation - by the look of the output picture it just extrapolates.
jdemeyer
commented
6 years ago There is some discussion also on #12798 about NaN coordinates and even a patch.
strogdon
commented
6 years ago Replying to @jdemeyer:
There is some discussion also on #12798 about
NaNcoordinates and even a patch.
The patch does remove the NaN values but in so doing it appears the surface is cropped such that it is not displayed over the entire convex hull.
jdemeyer
commented
6 years ago Replying to @strogdon:
in so doing it appears the surface is cropped such that it is not displayed over the entire convex hull.
Maybe, but I wouldn't try to fix that here. It would be a non-trivial change to change the triangulate() method to better take into account the input points. That is not something that we should fix in this blocker ticket.
strogdon
commented
6 years ago Replying to @jdemeyer:
Replying to @strogdon:
in so doing it appears the surface is cropped such that it is not displayed over the entire convex hull.
Maybe, but I wouldn't try to fix that here. It would be a non-trivial change to change the
triangulate()method to better take into account the input points. That is not something that we should fix in this blocker ticket.
Does it help your situation? I can't really test whether there is a time savings. I can only verify that the NaN are stripped.
strogdon
commented
6 years ago Replying to @strogdon:
The patch does remove the
NaNvalues but in so doing it appears the surface is cropped such that it is not displayed over the entire convex hull.
That is, most of them are gone. There are still a few corner ones left.
kiwifb
commented
6 years ago Replying to @strogdon:
Replying to @strogdon:
The patch does remove the
NaNvalues but in so doing it appears the surface is cropped such that it is not displayed over the entire convex hull.That is, most of them are gone. There are still a few
cornerones left.
Can you elaborate on that. I thought NaN were not displayed anyway, so I didn't expect a difference before and after stripping them.
strogdon
commented
6 years ago Replying to @kiwifb:
Replying to @strogdon:
Replying to @strogdon:
The patch does remove the
NaNvalues but in so doing it appears the surface is cropped such that it is not displayed over the entire convex hull.That is, most of them are gone. There are still a few
cornerones left.Can you elaborate on that. I thought
NaNwere not displayed anyway, so I didn't expect a difference before and after stripping them.
Basically, from my understanding, the stripping is an attempt to find the maximal rectangle that fits inside the convex hull and the 3D surface is drawn over this rectangle. The procedure sometimes fails, most notably at the corners of the constructed rectangle.
strogdon
commented
6 years ago Attachment: before_after_stripping_nan.patch.gz
before and after stripping NaN patch
strogdon
commented
6 years ago Patch to see changes before and after stripping the NaNs. Apply as patch -p1 < before_after_stripping_nan.patch and then rebuild sage, ./sage -b. To test use that in
comment:26 without the timeit.
kiwifb
commented
6 years ago Yes, I can see there is some differences. What does the plotting do with NaN?
strogdon
commented
6 years ago Replying to @kiwifb:
Yes, I can see there is some differences. What does the plotting do with
NaN?
My understanding is that points that have associated values of NaN are not plotted. When the test is run there should be two plots. The plot where the NaN are stripped should be displayed above a nearly rectangular region, except possibly, near the corners of the region. It is at these corners where there are some, possibly remaining NaNs.
strogdon
commented
6 years ago From commit 950bdd8145d262e493321bef67d3d57d26f9c464 the changes to src/sage/plot/plot3d/list_plot3d.py contain
- T=delaunay.Triangulation(x,y)
- f=T.nn_interpolator(z)
- f.default_value=0.0
- j=numpy.complex(0,1)
- vals=f[ymin:ymax:j*num_points,xmin:xmax:j*num_points]
+ points=[[x[i],y[i]] for i in range(len(x))]
+ j = numpy.complex(0, 1)
+ f = interpolate.CloughTocher2DInterpolator(points,z)
from .parametric_surface import ParametricSurfaceSo if I'm not mistaken the old delaunay triagulation implementation fills points outside the convex hull with zeros, i.e. f.default_value=0.0. The same thing can be achieved with scipy.interpolate.CloughTocher2DInterpolator with the change
diff --git a/src/sage/plot/plot3d/list_plot3d.py b/src/sage/plot/plot3d/list_plot3d.py
index c0c578b99f..77aeb583ae 100644
--- a/src/sage/plot/plot3d/list_plot3d.py
+++ b/src/sage/plot/plot3d/list_plot3d.py
@@ -461,7 +461,7 @@ def list_plot3d_tuples(v, interpolation_type, texture, **kwds):
points=[[x[i],y[i]] for i in range(len(x))]
j = numpy.complex(0, 1)
- f = interpolate.CloughTocher2DInterpolator(points,z)
+ f = interpolate.CloughTocher2DInterpolator(points,z,fill_value=0.0)
from .parametric_surface import ParametricSurface
def g(x, y):
z = f([x, y])In my opinion the resulting plot is not particularly pleasing but it does reproduce the old behavior. I'm unable to determine whether there is any CPU savings. I do believe that modern graphing algorithms generate these NaNs as a mechanism to not plot points and that leaving things as-is is what should be done. In which case the doctest could be marked as # long time until the older hardware that doesn't like the NaNs is gone. So I would favor either of the above two approaches instead of stripping the NaNs.
kiwifb
commented
6 years ago I must say that in a previous comment I mentioned the possibility of using fill_value. If 0 reproduce the old behavior we could go with that but I don't think it is an especially good or always appropriate value. As you said if NaN means do not plot, then it is more appropriate.
The problem is not with older hardware, power8 is not especially old, but its handling of NaN is currently poor. I don't know if it can be improved at the software level eventually but I would hope so.
strogdon
commented
6 years ago Replying to @kiwifb:
I must say that in a previous comment I mentioned the possibility of using
fill_value. If0reproduce the old behavior we could go with that but I don't think it is an especially good or always appropriate value. As you said ifNaNmeans do not plot, then it is more appropriate.The problem is not with older hardware, power8 is not especially old, but its handling of NaN is currently poor. I don't know if it can be improved at the software level eventually but I would hope so.
I couldn't check things with vanilla Sage. But I was able to install MPL-1.5.3 on sage-on-gentoo and I could manually replicate the current behavior by setting f.default_value=numpy.float('nan') with the old delaunay triangulation.
jdemeyer
commented
6 years ago Replying to @strogdon:
I do believe that modern graphing algorithms generate these
NaNs as a mechanism to not plot points and that leaving things as-is is what should be done.
-1.
Plotting NaNs makes no sense at all. It can only lead to garbage in, garbage out.
I am actually surprised that removing the NaNs changes the output. I haven't looked at the code, but it makes me suspect that maybe the NaN removal is not done correctly.
strogdon
commented
6 years ago Replying to @jdemeyer:
Plotting
NaNs makes no sense at all. It can only lead to garbage in, garbage out.
Try it!
sage: import matplotlib.pyplot as plt
sage: fig, (ax1, ax2) = plt.subplots(1,2)
sage: ax1.plot([1, 2, 2, 2, 3], [1, 1, NaN, 2, 2]);
sage: ax2.plot([1, 2, 2, 3], [1, 1, 2, 2]);
sage: plt.show()There may be other ways, but both scipy and matplotlib know how to handle such things.
jdemeyer
commented
6 years ago What's your point? That example is showing a Line2D object which is a high-level object: it's a collection of lines, which the low-level renderer will treat as individual lines to be drawn.
It's precisely the transformation of a high-level object (the input to list_plot3d) to a low-level object (a collection of triangles) that is going wrong in this ticket.
jdemeyer
commented
6 years ago In other words: the bug is not about a NaN appearing in the input of list_plot3d. It is about a NaN appearing in the processing of otherwise valid input and being sent to the rendering engine.
strogdon
commented
6 years ago Replying to @jdemeyer:
In other words: the bug is not about a
NaNappearing in the input oflist_plot3d. It is about aNaNappearing in the processing of otherwise valid input and being sent to the rendering engine.
OK, so I guess there could be a problem inside ParametricSurface which receives input that contains the NaNs. A big task to fix that. So perhaps we should do as was done previously with the old code until ParametricSurface can be investigated, i.e. fill points outside the convex hull with zeros? I believe we know how to do that with CloughTocher2DInterpolator.
jdemeyer
commented
6 years ago Replying to @strogdon:
OK, so I guess there could be a problem inside
ParametricSurfacewhich receives input that contains theNaNs. A big task to fix that.
I still think that it should be possible to just remove all the triangles containing a NaN. That can't be too hard.
Both #12798 and the matplotlib upgrade (#23696) cause this:
on hardware which is slow in computing with
NaNvalues.This plot is done by tachyon but in the input to tachyon, there are many
NaNs. This can be seen by applying attachment: nancount.patch. The result isCC: @strogdon @kiwifb
Component: graphics
Author: Jeroen Demeyer
Branch/Commit:
4d17a73Reviewer: Steven Trogdon
Issue created by migration from https://trac.sagemath.org/ticket/13135