# [SciPy-user] Recommended SVN version?

jason-sage@creativetra... jason-sage@creativetra...
Wed Nov 12 21:01:25 CST 2008

First of all, thanks for the tremendous work everyone has done!

In the Sage project (http://www.sagemath.org), we have a slightly
patched version of scipy 0.6 currently.  We recently upgraded to numpy
1.2 and would like to match that with an upgrade of scipy.  We are using
scipy more and more; for example, in our next version out later this
week, we switched our floating point and complex matrices to use a
numpy/scipy backend for most calculations.

Is there a recommended SVN version that we should update to while
waiting for 0.7 to be released?  We're looking for an SVN revision that
is relatively stable.  Incidentally, count me into the crowd that would
find scipy much more valuable if there were more frequent releases; Sage
users in general would be testing the code and giving feedback as well.

Also, we noticed the following behavior in our current version of scipy,
but only on an OSX 10.5 box.  If it's easy, can someone see if the
following commands give the spurious result we see below for the inverse
matrix with scipy.linalg.inv on an OSX 10.5 box?  I'd test it, but the
code works on our old scipy on my 32 bit Ubuntu box.

sage: import numpy
sage: a=numpy.array([[1,2,3],[4,5,6],[7,8,9]],dtype="float64")
sage: import scipy
sage: import scipy.linalg
sage: import numpy.linalg
sage: scipy.linalg.det(a)
0.0
sage: scipy.linalg.inv(a)
array([[ -4.50359963e+15,   9.00719925e+15,  -4.50359963e+15],
[  9.00719925e+15,  -1.80143985e+16,   9.00719925e+15],
[ -4.50359963e+15,   9.00719925e+15,  -4.50359963e+15]])
sage: numpy.linalg.det(a)
6.6613381477509392e-16
sage: numpy.linalg.inv(a)
array([[ -4.50359963e+15,   9.00719925e+15,  -4.50359963e+15],
[  9.00719925e+15,  -1.80143985e+16,   9.00719925e+15],
[ -4.50359963e+15,   9.00719925e+15,  -4.50359963e+15]])

Thanks,

Jason