[sage-support] saving a java3d figure
Hi all! How does one save an image created using java3d. Eg., T=Torus(1, .5, color='red',opacity=0.3) T.show(aspect_ratio=1,frame=False,viewer='java3d') displays a beautiful, interactive torus. But how can I save it? thanks, Jesse -- To post to this group, send email to sage-support@googlegroups.com To unsubscribe from this group, send email to sage-support+unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/sage-support URL: http://www.sagemath.org
[sage-support] Re: sage graph -- why is c_graph so slow??
Nathann, Many thanks! You're awesome! I looked at the patch, it looks tiny. :) I'll try to test it asap. Having some patch application issues. :-| Just in case you're familiar with this, I'll throw it out there. But this is probably a job for google: cd /Applications/sage/devel/sage hg import /Users/jberwald/src/ trac_12235.patch applying /Users/jberwald/src/trac_12235.patch patching file sage/graphs/base/c_graph.pyx Hunk #1 FAILED at 2977 1 out of 1 hunks FAILED -- saving rejects to file sage/graphs/base/ c_graph.pyx.rej patching file sage/graphs/digraph.py Hunk #1 FAILED at 2561 1 out of 1 hunks FAILED -- saving rejects to file sage/graphs/ digraph.py.rej abort: patch failed to apply Thanks! Jesse On Dec 29, 10:48 am, Nathann Cohen nathann.co...@gmail.com wrote: Hell !!! and still much faster than the c_graph implementation. Well... I spent *quite* some time over this problem, wrote a LOT of code and documentation , to find out later that this could be solved in a *very small* patch. I hope all the work I did could be used later on anyway, but for the moment there should be no further worries about this SCC method. I created a patch for this just there [1], which you will find with some benchmarks. http://trac.sagemath.org/sage_trac/ticket/12235 As a side note, I've also been testing subgraph functionality. Eg., self.M.subgraph(self.rand_verts(K)), which maybe has a better implementation using subgraph_search() ?? Nonononono ! This subgraph method has nothing to do with subgraph_search ! The subgraph method takes as an argument a set of vertices and returns the graph induced by those vertices. The subgraph_search (and all the subgraph_search_* method) take as an argument *another graph*, and look for copies of this other graph inside of the first one. Which is dead harder :-D Anyways, I greatly appreciate your help with this. It would be great to be able to use Sage/Python to run all of our code. Please complain whenever you have the slightest thought that Sage may not be the best graph library in the world :-p Have fuun ! :-p Nathann -- To post to this group, send email to sage-support@googlegroups.com To unsubscribe from this group, send email to sage-support+unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/sage-support URL: http://www.sagemath.org
[sage-support] Re: sage graph -- why is c_graph so slow??
Hi Nathann, Thank you for the timely updates! I agree with you about the calls to random. I can move those out of the timing portion. I suspected that the passage to the backend was probably responsible for the slow speed of the add/remove edge/vertices calls. The cost of method calls is overhead I'm willing to swallow if the harder algorithms are faster. :) As for the scc() method, the _digraph() problem was my fault--I didn't understand how scc_digraph() worked. Unfortunately, after removing _digraph() the timings are just as bad. Sample below: (1000 node graph) scc() -- 10 trials: sage_cgraph 1362871.89 usec [ now computed using self.M.strongly_connected_components() (see previous post in thread) ] scc() -- 10 trials: networkx 6015.06 usec I'm afraid I'm still missing something crucial here? :-? Also, as a confirmation of your argument concerning the calls to the backend, the Sage implementation of NetworkX (implementation='networkx' instead of 'c_graph') does work nearly as as fast as pure NetworkX after removing _digraph()--and still much faster than the c_graph implementation. As far as which functions/methods to benchmark, I am (or rather we are) interested in a few specific graph algorithms, scc() among them. That's why I've been picking on scc(). As a side note, I've also been testing subgraph functionality. Eg., self.M.subgraph(self.rand_verts(K)), which maybe has a better implementation using subgraph_search() ?? Anyways, I greatly appreciate your help with this. It would be great to be able to use Sage/Python to run all of our code. Merry Christmas,Jesse On Dec 25, 3:08 am, Nathann Cohen nathann.co...@gmail.com wrote: Oh yes, and something else about your benchmark : try to avoid using rand methods when you are doing one, especially when you test such low-level methods, because often the rand() method represents an important part of the time. The best would be to compute all the random number you need in a first phase, then run %timeit on the add_edge part. Well, it probably will not reflect well on Sage because it should increase the differences between the libraries, but I think that it is very important in your benchmark, To give you an idea : sage: from numpy import random as rnd sage: sage: g = Graph(500) sage: def rand_entry(G): : ... n = G.order() : ... i = rnd.randint(0,n-1) : ... j = rnd.randint(0,n-1) : ... G.add_edge(i,j) : ... G.delete_edge(i,j) : sage: def just_rand(G): : ... n = G.order() : ... i = rnd.randint(0,n-1) : ... j = rnd.randint(0,n-1) : ... return i*j : sage: %timeit rand_entry(g) 625 loops, best of 3: 20.4 µs per loop sage: %timeit just_rand(g) 625 loops, best of 3: 4.93 µs per loop So 20% of the time used by this test method is actualy used by calls to random() :-) Nathann -- To post to this group, send email to sage-support@googlegroups.com To unsubscribe from this group, send email to sage-support+unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/sage-support URL: http://www.sagemath.org
[sage-support] Re: sage graph -- why is c_graph so slow??
Hi Nathan,
Thanks for looking into this. I believe that I stayed within Sage's
library when I wrote my test code. The general outline and some
classes were originally written by a collaborator (I don't want to
take credit, but I'll take responsibility where there are errors!) I
couldn't find a way to attach files, so I've pasted the two classes
below (plus a base class):
SAGE
sage_cgraph_tester.py:
import numpy as np
from numpy import random as rnd
from sage.all import DiGraph
from tester import Tester
class Tester_sage_cgraph( Tester ):
def __init__(self):
self.name = 'sage_cgraph'
def set_graph(self,E,N):
self.N = N
self.M = DiGraph(N, implementation=networkx ) #c_graph,
sparse=True)
self.M.add_edges(E)
def rand_row(self,R):
i = self.rand_vert()
self.M.add_edges([(i,self.rand_vert()) for j in range(R)])
def rand_col(self,R):
i = self.rand_vert()
self.M.add_edges([(self.rand_vert(),i) for j in range(R)])
def rand_entry(self):
i = self.rand_vert()
j = self.rand_vert()
if not self.M.has_edge(i,j):
self.M.add_edge(i,j)
def subgraph(self,K):
self.M.subgraph(self.rand_verts(K))
def scc(self):
self.M.strongly_connected_components_digraph()
NETWORKX
networkx_tester.py (originally coded by my collaborator, but I'll take
full responsibility for any errors :) ):
import numpy as np
from numpy import random as rnd
import networkx
from tester import Tester
import rads_nx
class Tester_networkx(Tester):
def __init__(self):
self.name = 'networkx'
def set_graph(self,E,N):
self.N = N
self.M = networkx.DiGraph()
self.M.add_nodes_from(range(N))
self.M.add_edges_from(E)
def rand_row(self,R):
i = self.rand_vert()
self.M.add_edges_from([(i,self.rand_vert()) for j in range(R)])
def rand_col(self,R):
i = self.rand_vert()
self.M.add_edges_from([(self.rand_vert(),i) for j in range(R)])
def rand_entry(self):
i = self.rand_vert()
j = self.rand_vert()
if not self.M.has_edge(i,j):
self.M.add_edge(i,j)
def subgraph(self,K):
networkx.subgraph(self.M,self.rand_verts(K))
def scc(self):
networkx.algorithms.components.strongly_connected_components(self.M)
The base class Tester is in tester.py:
import numpy as np
from numpy import random as rnd
class Tester:
def rand_vert(self):
return rnd.randint(0,self.N-1)
def rand_verts(self,R):
return rnd.randint(0,self.N-1,R)
The NetworkX and Sage graph testers are initialize as follows:
G = networkx.read_adjlist('testmat%i.adjlist' % (N))
N = len(G.nodes())
E = G.edges()
E = map(lambda x: (int(x[0]),int(x[1])), E)
print 'initializing testers... ',
tester.set_graph(E,N)
Operations are timed using the timeit module. Eg.,
test_str = 'testers[%i].%s(%s)' % ( tester, test['F'],test['args'] )
timer = timeit.Timer( test_str, import_str),
where test['F'] is the method names and test['args'] contain any
necessary args.
Thanks again for your help.
Happy holidays!
Jesse
On Dec 23, 6:43 pm, Nathann Cohen nathann.co...@gmail.com wrote:
Hello Jesse !
Well, for a start it wouldn't be very fair to compare graph libraries if
you do not use our graph methods and recode your own ! You seem to have
rewritten your version of strongly connected components to test the
libraries, and such low-level methods are in Sage written in Cython, so
this kind of running times are only those you would get if you use Sage
graphs but refuse to use any of the methods present in the library :-D
This being said, I just did some tests and if they are far from being as
bad for Sage as yours are, I was quite disappointed myself. I was under the
impression we were leaving NetworkX far behind, and it looks like we
actually are behind in some cases, which will need to be fixed. Could I ask
you to provide examples of codes which have different running times for
NetworkX and Sage ? I guess you only use the add/remove edge/vertices
methods in your code, which may be the explanation. When you are doing that
you are actually calling Cython methods through Python functions, and
spending more time calling methods than actually getting the job done
Though to be honest I do not want to have to explain why Sage is slower, I
would like to show that it is faster :-)
Hence, if you can provide the code, we could begin to talk about the
technical reasons.
Good night !
Nathann
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[sage-support] sage graph -- why is c_graph so slow??
Hi all,
I'm benchmarking some graph libraries. I was excited to see that
Sage's graph library has a backend implemented in Cython.
Unfortunately, it seems to be orders of magnitude slower than a pure
NetworkX implementation. Here a code summary:
import networkx
# read in test adjacency matrix using networkx
G = networkx.read_adjlist('testmat%i.adjlist' % (N))
N = len(G.nodes())
E = G.edges()
E = map(lambda x: (int(x[0]),int(x[1])), E)
# set up test object
tester.set_graph(E,N)
The tester object is either of two classes, Tester_networkx or
Tester_sage_cgraph, both of which set up the graph G in their
respective implementations. For instance, if N is the number of nodes,
tester is initialized as follows:
import networkx
...
self.M = networkx.DiGraph()
self.M.add_edges_from( range(N) )
self.M.add_edges(E)
or
from sage.all import DiGraph
…
self.M = DiGraph(N, implementation=c_graph) # tried sparse=True,
similar results
self.M.add_edges(E)
I am seeing the following timing results for the two different
implementations:
On a 1000 node graph, adding edges from node i to 100 randomly chosen
nodes (slowest of 100 trials):
networkx 298.02 usec
sage_cgraph 749.83 usec
Things get very bad when looking for strongly connected components
(slowest of 10 trials):
scc() -- 10 trials:
networkx5846.98 usec
scc() -- 10 trials:
sage_cgraph 1363383.05 usec (231x networkx)
I tried changing the Sage Graph implementation to networkx, hoping
to see identical behavior to the pure NetworkX version. Unfortunately,
it is somewhere in between:
scc() -- 10 trials:
sage_networkx 104291.92 usec (20x networkx)
I'm assuming I must be doing something wrong to see such large
differences. Any ideas??
Thanks for your help,
Jesse
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[sage-support] An error occurred while installing python-2.6.4.p11
Hi all, I'm attempting to build Sage on a SUSE Enterprise 10 (SLES 10) cluster. I have run ./configure as follows: ./configure SAGE_FORTRAN_LIB=/usr/lib/libgfortran.so.1 SAGE_FORTRAN=/ usr/bin/gfortran CXX=g++ CC=gcc The error is related to the install of python2.6. Why did configure choose /local/scr/jberwald/TMPDIR/poG7ZVuA? Any help would be greatly appreciated. Thanks, Jesse from install.log Extracting package /sciclone/home04/jberwald/src/sage-4.7.2/spkg/ standard/python-2.6.4.p11.spkg ... -rw-r- 1 jberwald mathf 11748536 2011-07-05 07:28 /sciclone/home04/ jberwald/src/sage-4.7.2/spkg/standard/python-2.6.4.p11.spkg Finished extraction Host system uname -a: Linux ty71 2.6.16.53-0.16-smp #1 SMP Tue Oct 2 16:57:49 UTC 2007 x86_64 x86_64 x86_64 GNU/Linux CC Version gcc -v Using built-in specs. Target: x86_64-suse-linux Configured with: ../configure --enable-threads=posix --prefix=/usr -- with-local-prefix=/usr/local --infodir=/usr/share/info --mandir=/usr/ share/man --libdir=/usr/lib64 --libexecdir=/usr/lib64 --enable- languages=c,c++,objc,fortran,obj-c++,java,ada --enable- checking=release --with-gxx-include-dir=/usr/include/c++/4.1.2 -- enable-ssp --disable-libssp --disable-libgcj --with-slibdir=/lib64 -- with-system-zlib --enable-shared --enable-__cxa_atexit --enable- libstdcxx-allocator=new --program-suffix= --enable-version-specific- runtime-libs --without-system-libunwind --with-cpu=generic -- host=x86_64-suse-linux Thread model: posix gcc version 4.1.2 20070115 (prerelease) (SUSE Linux) Patching src/setup.py for multiarch. patch: Can't create file /local/scr/jberwald/TMPDIR/poG7ZVuA : Permission denied Error patching src/setup.py real0m0.011s user0m0.000s sys 0m0.004s sage: An error occurred while installing python-2.6.4.p11 -- To post to this group, send email to sage-support@googlegroups.com To unsubscribe from this group, send email to sage-support+unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/sage-support URL: http://www.sagemath.org
[sage-support] Re: An error occurred while installing python-2.6.4.p11
I meant to say make instead of ./configure. On Dec 8, 9:52 am, entropy jberw...@gmail.com wrote: Hi all, I'm attempting to build Sage on a SUSE Enterprise 10 (SLES 10) cluster. I have run ./configure as follows: ./configure SAGE_FORTRAN_LIB=/usr/lib/libgfortran.so.1 SAGE_FORTRAN=/ usr/bin/gfortran CXX=g++ CC=gcc The error is related to the install of python2.6. Why did configure choose /local/scr/jberwald/TMPDIR/poG7ZVuA? Any help would be greatly appreciated. Thanks, Jesse from install.log Extracting package /sciclone/home04/jberwald/src/sage-4.7.2/spkg/ standard/python-2.6.4.p11.spkg ... -rw-r- 1 jberwald mathf 11748536 2011-07-05 07:28 /sciclone/home04/ jberwald/src/sage-4.7.2/spkg/standard/python-2.6.4.p11.spkg Finished extraction Host system uname -a: Linux ty71 2.6.16.53-0.16-smp #1 SMP Tue Oct 2 16:57:49 UTC 2007 x86_64 x86_64 x86_64 GNU/Linux CC Version gcc -v Using built-in specs. Target: x86_64-suse-linux Configured with: ../configure --enable-threads=posix --prefix=/usr -- with-local-prefix=/usr/local --infodir=/usr/share/info --mandir=/usr/ share/man --libdir=/usr/lib64 --libexecdir=/usr/lib64 --enable- languages=c,c++,objc,fortran,obj-c++,java,ada --enable- checking=release --with-gxx-include-dir=/usr/include/c++/4.1.2 -- enable-ssp --disable-libssp --disable-libgcj --with-slibdir=/lib64 -- with-system-zlib --enable-shared --enable-__cxa_atexit --enable- libstdcxx-allocator=new --program-suffix= --enable-version-specific- runtime-libs --without-system-libunwind --with-cpu=generic -- host=x86_64-suse-linux Thread model: posix gcc version 4.1.2 20070115 (prerelease) (SUSE Linux) Patching src/setup.py for multiarch. patch: Can't create file /local/scr/jberwald/TMPDIR/poG7ZVuA : Permission denied Error patching src/setup.py real 0m0.011s user 0m0.000s sys 0m0.004s sage: An error occurred while installing python-2.6.4.p11 -- To post to this group, send email to sage-support@googlegroups.com To unsubscribe from this group, send email to sage-support+unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/sage-support URL: http://www.sagemath.org
